Methodological Review: Temporal reasoning with medical data-A review with emphasis on medical natural language processing
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paper_title: Temporal representation and reasoning in artificial intelligence: A review

paper_content:

The explicit representation and reasoning about time is an important problem in many areas of artificial intelligence. Over the last 10-15 years, it has been attracting the attention of many researchers. Several temporal reasoning systems, differing in design issues related to ontology of time, underlying temporal logic, temporal constraints used and algorithms employed, have been developed. In this survey, important representational issues which determine a temporal reasoning system are introduced. In particular, several important notions like change, causality, actions are described in terms of time. For each issue different choices available in the literature are discussed. The most influential approaches to temporal reasoning in artificial intelligence are analyzed in terms of these major representational issues.
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paper_title: Temporal Reasoning and Temporal Data Maintenance in Medicine: Issues and Challenges

paper_content:

Abstract We present a brief, nonexhaustive overview of research efforts in designing and developing time-oriented systems in medieine. The growing volume of research on time-oriented systems in medicine can be viewed from either an application point of view, focusing on different generic tasks (e.g. diagnosis) and clinical areas (e.g. cardiology), or from a methodological point of view, distinguishing between different theoretical approaches. In this overview, we focus on highlighting methodological and theoretical choices, and conclude with suggestions for new research directions. Two main research directions can be noted: temporal reasoning, which supports various temporal inference tasks (e.g. temporal abstraction, time-oriented decision support, forecasting, data validation), and temporal data maintenance, which deals with storage and retrieval of data that have heterogeneous temporal dimensions. Efforts common to both research areas include the modeling of time, of temporal entities, and of temporal queries. We suggest that tasks such as abstraction of time-oriented data and the handling of different temporal-granularity levels should provide common ground for collaboration between the two research directions and fruitful areas for future research.
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paper_title: Temporal representation and reasoning in artificial intelligence: Issues and approaches

paper_content:

Time is one of the most relevant topics in AI. It plays a major role in several areas, ranging from logical foundations to applications of knowledgedbased systems. In this paper, we survey a wide range of research in temporal representation and reasoning, without committing ourselves to the point of view of any specific application. The organization of the paper follows the commonly recognized division of the field in two main subfields: reasoning about actions and change, and reasoning about temporal constraints. We give an overview of the basic issues, approaches, and results in these two areas, and outline relevant recent developments. Furthermore, we briefly analyze the major emerging trends in temporal representation and reasoning as well as the relationships with other welldestablished areas, such as temporal databases and logic programming.
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paper_title: Natural Language and Text Processing in Biomedicine

paper_content:

After reading this chapter, you should know the answers to these questions: ::: ::: ::: Why is natural language processing (NLP) important? ::: ::: ::: What are the potential uses for NLP in the biomedical domain? ::: ::: ::: What forms of knowledge are used in NLP? ::: ::: ::: What are the principal techniques of NLP? ::: ::: ::: What are the challenges for NLP in the clinical domain? ::: ::: ::: What are the challenges for NLP in the biological domain?
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paper_title: A temporal constraint structure for extracting temporal information from clinical narrative

paper_content:

Introduction. Time is an essential element in medical data and knowledge which is intrinsically connected with medical reasoning tasks. Many temporal reasoning mechanisms use constraint-based approaches. Our previous research demonstrates that electronic discharge summaries can be modeled as a simple temporal problem (STP).Objective. To categorize temporal expressions in clinical narrative text and to propose and evaluate a temporal constraint structure designed to model this temporal information and to support the implementation of higher-level temporal reasoning.Methods. A corpus of 200 random discharge summaries across 18 years was applied in a grounded approach to construct a representation structure. Then, a subset of 100 discharge summaries was used to tally the frequency of each identified time category and the percentage of temporal expressions modeled by the structure. Fifty random expressions were used to assess inter-coder agreement.Results. Six main categories of temporal expressions were identified. The constructed temporal constraint structure models time over which an event occurs by constraining its starting time and ending time. It includes a set of fields for the endpoint(s) of an event, anchor information, qualitative and metric temporal relations, and vagueness. In 100 discharge summaries, 1961 of 2022 (97%) identified temporal expressions were effectively modeled using the temporal constraint structure. Inter-coder evaluation of 50 expressions yielded exact match in 90%, partial match with trivial differences in 8%, partial match with large differences in 2%, and total mismatch in 0%.Conclusion. The proposed temporal constraint structure embodies a sufficient and successful implementation method to encode the diversity of temporal information in discharge summaries. Placing data within the structure provides a foundational representation upon which further reasoning, including the addition of domain knowledge and other post-processing to implement an STP, can be accomplished.
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paper_title: Temporal reasoning for decision support in medicine

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Objective: Handling time-related concepts is essential in medicine. During diagnosis it can make a substantial difference to know the temporal order in which some symptoms occurred or for how long they lasted. During prognosis the potential evolutions of a disease are conceived as a description of events unfolding in time. In therapy planning the different steps of treatment must be applied in a precise order, with a given frequency and for a certain span of time in order to be effective. This article offers a survey on the use of temporal reasoning for decision support-related tasks in medicine.Material and methods: Key publications of the area, mainly circumscribed to the latest two decades, are reviewed and classified according to three important stages of patient treatment requiring decision support: diagnosis, prognosis and therapy planning/management. Other complementary publications, like those on time-centered information storage and retrieval, are also considered as they provide valuable support to the above mentioned three stages.Results: Key areas are highlighted and used to organize the latest contributions. The survey of previous research is followed by an analysis of what can still be improved and what is needed to make the next generation of decision support systems for medicine more effective.Conclusions: It can be observed that although the area has been considerably developed, there are still areas where more research is needed to make time-based systems of widespread use in decision support-related areas of medicine. Several suggestions for further exploration are proposed as a result of the survey.
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paper_title: Time is Everything: Time-Oriented Clinical Information Systems

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Abstract ::: Time is important in clinical information systems. Representing, maintaining, querying, and reasoning about time-oriented clinical data is a major theoretical and practical research area in medical informatics. In this nonexhaustive overview, we present a brief synopsis of research efforts in designing and developing time-oriented information systems in medicine. These efforts can be viewed from either an application point of view, distinguishing between different clinical tasks (such as diagnosis versus therapy) and clinical areas (such as infectious diseases versus oncology), or a methodological point of view, distinguishing between different theoretical approaches. We also explore the two primary methodological and theoretical paths research has taken in the past decade: temporal reasoning and temporal data maintenance. Both of these research areas include efforts to model time, temporal entities, and temporal queries. Collaboration between the two areas is possible, through tasks such as the abstraction of raw time-oriented clinical data into higher-level meaningful clinical concepts and the management of different levels of temporal granularity. Such collaboration could provide a common ground and useful areas for future research and development. We conclude with our view of future research directions.
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paper_title: Temporal consistency checking in clinical guidelines acquisition and execution: the GLARE’s approach

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GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent system for the acquisition, representation and execution of clinical guidelines. Temporal constraints play an important role within clinical guidelines (e.g. to specify therapies). The treatment of such constraints is one of the distinguishing features of GLARE. During acquisition, GLARE supports (i) the representation and (ii) the checking of the consistency of the temporal constraints. Moreover, it (iii) automatically checks that the times of execution of specific actions respect the general temporal constraints described in the guideline. This treatment of temporal constraints involves the extension of various Artificial Intelligence (AI) techniques.
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paper_title: Temporal representation and reasoning in artificial intelligence: A review

paper_content:

The explicit representation and reasoning about time is an important problem in many areas of artificial intelligence. Over the last 10-15 years, it has been attracting the attention of many researchers. Several temporal reasoning systems, differing in design issues related to ontology of time, underlying temporal logic, temporal constraints used and algorithms employed, have been developed. In this survey, important representational issues which determine a temporal reasoning system are introduced. In particular, several important notions like change, causality, actions are described in terms of time. For each issue different choices available in the literature are discussed. The most influential approaches to temporal reasoning in artificial intelligence are analyzed in terms of these major representational issues.
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paper_title: A framework for knowledge-based temporal abstraction

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A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events and contexts. A formal specification of a domain's temporal abstraction knowledge supports acquisition, maintenance, reuse and sharing of that knowledge. ::: ::: The knowledge-based temporal abstraction method decomposes the temporal abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal abstraction mechanisms. The temporal abstraction mechanisms depend on four domain-specific knowledge types: structural, classification (functional), temporal semantic (logical) and temporal dynamic (probabilistic) knowledge. Domain values for all knowledge types are specified when a temporal abstraction system is developed. ::: ::: The knowledge-based temporal abstraction method has been implemented in the RESUME system and has been evaluated in several clinical domains (protocol-based care, monitoring of children's growth and therapy of diabetes) and in an engineering domain (monitoring of traffic control), with encouraging results.
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paper_title: TQuery: a context-sensitive temporal query language.

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Users of electronic medical databases request pertinent information by recasting their clinical questions into a formal database query language. Because the query language is the user's only access to the data, the query language must be powerful enough to enable users to express their data requirements. However, a competing need is for the query language to be restrictive enough so that queries can have unambiguous semantics and the query processor can generate correct answers. We describe a query language, called TQuery , that was designed specifically to formulate database queries that are dependent on temporal and contextual relationships. TQuery specifications express contextual constraints without the need to explicitly reference calendar dates. TQuery is the database query language used to retrieve patient data from an object-oriented electronic patient medical-record system called the temporal network (TNET). TNET and TQuery were developed to support the real-time temporal reasoning and representation needs of a LISP workstation-based medical expert system.
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paper_title: A Logical Foundation for Representation of Clinical Data

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Objective: A general framework for representation of clinical data that provides a declarative semantics of terms and that allows developers to define explicitly the relationships among both terms and combinations of terms. Design: Use of conceptual graphs as a standard representation of logic and of an existing standardized vocabulary, the Systematized Nomenclature of Medicine (SNOMED International), for lexical elements. Concepts such as time, anatomy, and uncertainty must be modeled explicitly in a way that allows relation of these foundational concepts to surface-level clinical descriptions in a uniform manner. Results: The proposed framework was used to model a simple radiology report, which included temporal references. Conclusion: Formal logic provides a framework for formalizing the representation of medical concepts. Actual implementations will be required to evaluate the practicality of this approach.
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paper_title: Temporal Expressiveness in Querying a Time-stamp—based Clinical Database

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Most health care databases include time-stamped instant data as the only temporal representation of patient information. Many previous efforts have attempted to provide frameworks in which medical databases could be queried in relation to time. These, however, have required either a sophisticated database representation of time, including time intervals, or a time-stamp-based database coupled with a nonstandard temporal query language. In this work, the authors demonstrate how their previously described data retrieval application, DXtractor, can be used as a database querying application with expressive power close to that of temporal databases and temporal query languages, using only standard SQL and existing time- stamp-based repositories. DXtractor provides the ability to compose temporal queries through an interface that is understood by nonprogramming medical personnel. Not all temporal constructs are easily implemented using this framework; nonetheless, DXtractor's temporal capabilities provide a significant improvement in the temporal expressivity accessible to clinicians using standard time-stamped clinical databases. n JAMIA. 2000;7:152-163.
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paper_title: Temporal Reasoning and Temporal Data Maintenance in Medicine: Issues and Challenges

paper_content:

Abstract We present a brief, nonexhaustive overview of research efforts in designing and developing time-oriented systems in medieine. The growing volume of research on time-oriented systems in medicine can be viewed from either an application point of view, focusing on different generic tasks (e.g. diagnosis) and clinical areas (e.g. cardiology), or from a methodological point of view, distinguishing between different theoretical approaches. In this overview, we focus on highlighting methodological and theoretical choices, and conclude with suggestions for new research directions. Two main research directions can be noted: temporal reasoning, which supports various temporal inference tasks (e.g. temporal abstraction, time-oriented decision support, forecasting, data validation), and temporal data maintenance, which deals with storage and retrieval of data that have heterogeneous temporal dimensions. Efforts common to both research areas include the modeling of time, of temporal entities, and of temporal queries. We suggest that tasks such as abstraction of time-oriented data and the handling of different temporal-granularity levels should provide common ground for collaboration between the two research directions and fruitful areas for future research.
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paper_title: Time and time again: The many ways to represent time

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One of the most crucial problems in any computer system that involves representing the world is the representation of time. This includes applications such as databases, simulation, expert systems, and applications of Artificial Intelligence in general. In this brief article, I will give a survey of the basic techniques available for representing time, and then talk about temporal reasoning in a general setting as needed in AI applications. Quite different representations of time are usable depending on the assumptions that can be made about the temporal information to be represented. the most crucial issue is the degree of certainty one can assume. Can one assume that a timestamp can be assigned to each event, or barring that, that the events are fully ordered? Or can we only assume that a partial ordering of events is known? Can events be simultaneous? Can they overlap in time and yet not be simultaneous? If they are not instaneous, do we know the durations of events? Different answers to each of these questions allow very different representations of time.
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paper_title: The Chronus II Temporal Database Mediator

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Clinical databases typically contain a significant amount of temporal information. This information is often crucial in medical decision-support systems. Although temporal queries are common in clinical systems, the medical informatics field has no standard means for representing or querying temporal data. Over the past decade, the temporal database community has made a significant amount of progress in temporal systems. Much of this research can be applied to clinical database systems. This paper outlines a temporal database mediator called Chronus II. Chronus II extends the standard relational model and the SQL query language to support temporal queries. It provides an expressive general-purpose temporal query language that is tuned to the querying requirements of clinical decision support systems. This paper describes how we have used Chronus II to tackle a variety of clinical problems in decision support systems developed by our group.
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paper_title: The River of Time

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Preface to Russian edition Preface to English edition 1. Origins of thinking about time 2. Science of time is born 3. Light 4. The pace of time can be slowed down! 5. Time machine 6. Time, space and gravitation 7. Holes in space and time 8. Energy extracted from black holes 9. Towards the sources of Time River 10. Journey to unusual depths 11. Grand unification 12. Sources 13. What produces the flow of time and why in a single direction? 14. Against the flow 15. Can we change the past? Conclusion.
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paper_title: Maintaining knowledge about temporal intervals

paper_content:

An interval-based temporal logic is introduced, together with a computationally effective reasoning algorithm based on constraint propagation. This system is notable in offering a delicate balance between
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paper_title: Temporal representation and reasoning in artificial intelligence: Issues and approaches

paper_content:

Time is one of the most relevant topics in AI. It plays a major role in several areas, ranging from logical foundations to applications of knowledgedbased systems. In this paper, we survey a wide range of research in temporal representation and reasoning, without committing ourselves to the point of view of any specific application. The organization of the paper follows the commonly recognized division of the field in two main subfields: reasoning about actions and change, and reasoning about temporal constraints. We give an overview of the basic issues, approaches, and results in these two areas, and outline relevant recent developments. Furthermore, we briefly analyze the major emerging trends in temporal representation and reasoning as well as the relationships with other welldestablished areas, such as temporal databases and logic programming.
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paper_title: A logic-based calculus of events

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An Anthurium plant named 'Krypton' having small, heart shaped purple spathes with a purple spadix, with the spathes being carried above and among the foliage. The leaves are small, dark green and very shiny. The plant has a miniature growth habit, is highly branched, and is suitable for smaller pot sizes (10 cm). The plant grows to a marketable size and begins flowering quickly.
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paper_title: Some philosophical problems from the standpoint of artificial intelligence

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Abstract A computer program capable of acting intelligently in the world must have a general representation of the world in terms of which its inputs are interpreted. Designing such a program requires commitments about what knowledge is and how it is obtained. Thus, some of the major traditional problems of philosophy arise in artificial intelligence. More specifically, we want a computer program that decides what to do by inferring in a formal language that a certain strategy will achieve its assigned goal. This requires formalizing concepts of causality, ability, and knowledge. Such formalisms are also considered in philosophical logic. The first part of the paper begins with a philosophical point of view that seems to arise naturally once we take seriously the idea of actually making an intelligent machine. We go on to the notions of metaphysically and epistemo-logically adequate representations of the world and then to an explanation of can, causes, and knows in terms of a representation of the world by a system of interacting automata. A proposed resolution of the problem of freewill in a deterministic universe and of counterfactual conditional sentences is presented. The second part is mainly concerned with formalisms within which it can be proved that a strategy will achieve a goal. Concepts of situation, fluent, future operator, action, strategy, result of a strategy and knowledge are formalized. A method is given of constructing a sentence of first-order logic which will be true in all models of certain axioms if and only if a certain strategy will achieve a certain goal. The formalism of this paper represents an advance over McCarthy (1963) and Green (1969) in that it permits proof of the correctness of strategies that contain loops and strategies that involve the acquisition of knowledge; and it is also somewhat more concise. The third part discusses open problems in extending the formalism of part 2. The fourth part is a review of work in philosophical logic in relation to problems of artificial intelligence and a discussion of previous efforts to program ‘general intelligence’ from the point of view of this paper.
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paper_title: Time in history : the evolution of our general awareness of time and temporal perspective

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Part 1 Introduction: awareness of time - time and civil life, our sense of time, time and mankind describing time - time, language and number, time and natural bases of measurement, time in contemporary society. Part 2 Time in antiquity and the Middle Ages: time at the dawn of history - prehistory, ancient Egypt, Sumeria and Babylon, ancient Iran time in classical antiquity - classical Greece and Hellenistic civilization, ancient Israel, Imperial Rome and early Christendom time in the Middle Ages - medieval Europe, the Islamic world, the periodization of time and millenarianism, the measurement of time time in the Far East and Mesoamerica - India, China, the Maya. Part 3 Time in the modern world: the advent of the mechanical clock - the invention of the verge escapement, the social influence of the mechanical clock time and history in the Renaissance and the Scientific Revolution - reform of the calendar, the pendulum clock and the clocklike universe, attitudes to time and history in the 16th and 17th centuries time and history in the 18th century - the invention of the marine chronometer, the discovery of historical perspective evolution and the Industrial Revolution - the evolutionary universe, the role of time in modern industrial society rival concepts of time - instant and duration, relativistic and cosmic time civilization as progress? - time and belief in progress, time, history and the computerized society. Appendices: 1 - leap years 2 - the metonic calendrical cycle 3 - the calculation of Easter. References. Index.
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paper_title: A foundational model of time for heterogeneous clinical databases.

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Differences among the database representations of clinical data are a major barrier to the integration of databases and to the sharing of decision-support applications across databases. Prior research on resolving data heterogeneity has not addressed specifically the types of mismatches found in various timestamping approaches for clinical data. Such temporal mismatches, which include time-unit differences among timestamps, must be overcome before many applications can use these data to reason about diagnosis, therapy, or prognosis. In this paper, we present an analysis of the types of temporal mismatches that exist in databases. To formalize these various approaches to timestamping, we provide a foundational model of time. This model gives us the semantics necessary to encode the temporal dimensions of clinical data in legacy databases and to transform such heterogeneous data into a uniform temporal representation suitable for decision support. We have implemented this foundational model as an extension to our Chronus system, which provides clinical decision-support applications the ability to match temporal patterns in clinical databases. We discuss the uniqueness of our approach in comparison with other research on representing and querying clinical data with varying timestamp representations.
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paper_title: Medical Language Processing Computer Management Of Narrative Data

paper_content:

Thank you very much for reading medical language processing computer management of narrative data. Maybe you have knowledge that, people have search numerous times for their favorite novels like this medical language processing computer management of narrative data, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some malicious bugs inside their desktop computer.
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paper_title: A temporal query system for protocol-directed decision support.

paper_content:

Chronus is a query system that supports temporal extensions to the Structured Query Language (SQL) for relational databases. Although the relational data model can store time-stamped data and can permit simple temporal-comparison operations, it does not provide either a closed or a sufficient algebra for manipulating temporal data. In this paper, we outline an algebra that maintains a consistent relational representation of temporal data and that allows the type of temporal queries needed for protocol-directed decision support. We also discuss how Chronus can translate between our temporal algebra and the relational algebra used for SQL queries. We have applied our system to the task of screening patients for clinical trials. Our results demonstrate that Chronous can express sufficiently all required temporal queries, and that the search time of such queries is similar to that of standard SQL.
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paper_title: The Logical Approach to Temporal Reasoning

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Temporal reasoning started to be considered as a subject of study in artificial intelligence in the late 1970's. Since then several ways to represent and use temporal knowledge have been suggested. As a result of that, there are several formalisms capable of coping with temporal notions in some way or other. They range from isolated proposals to complex systems where the temporal aspect is used together with other important features for the task of modelling an intelligent agent. The purposes of this article are to summarize logic-based temporal reasoning research and give a glance on the different research tracks envisaging future lines of research. It is intended to be useful to those who need to be involved in systems having these characteristics and also an occasion to present newcomers some problems in the area that still waits for a solution.
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paper_title: Toward interactive scheduling systems for managing medical resources

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Managers of medico-hospital facilities are facing two general problems when allocating resources to activities: (1) to find an agreement between several and contrasting requirements; (2) to manage dynamic and uncertain situations when constraints suddenly change over time due to medical needs. This paper describes the results of a research aimed at applying constraint-based scheduling techniques to the management of medical resources. A mixed-initiative problem solving approach is adopted in which a user and a decision support system interact to incrementally achieve a satisfactory solution to the problem. A running prototype is described called Interactive Scheduler which offers a set of functionalities for a mixed-initiative interaction to cope with the medical resource management. Interactive Scheduler is endowed with a representation schema used for describing the medical environment, a set of algorithms that address the specific problems of the domain, and an innovative interaction module that offers functionalities for the dialogue between the support system and its user. A particular contribution of this work is the explicit representation of constraint violations, and the definition of scheduling algorithms that aim at minimizing the amount of constraint violations in a solution.
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paper_title: HMAP– A temporal data model managing intervals with different granularities and indeterminacy from natural language sentences

paper_content:

The granularity of given temporal information is the level of abstraction at which information is expressed. Different units of measure allow one to represent different granularities. Indeterminacy is often present in temporal information given at different granularities: temporal indeterminacy is related to incomplete knowledge of when the considered fact happened. Focusing on temporal databases, different granularities and indeterminacy have to be considered in expressing valid time, i.e., the time at which the information is true in the modeled reality. In this paper, we propose HMAP (The term is the transliteration of an ancient Greek poetical word meaning “day”.), a temporal data model extending the capability of defining valid times with different granularity and/or with indeterminacy. In HMAP, absolute intervals are explicitly represented by their start,end, and duration: in this way, we can represent valid times as “in December 1998 for five hours”, “from July 1995, for 15 days”, “from March 1997 to October 15, 1997, between 6 and 6:30 p.m.”. HMAP is based on a three-valued logic, for managing uncertainty in temporal relationships. Formulas involving different temporal relationships between intervals, instants, and durations can be defined, allowing one to query the database with different granularities, not necessarily related to that of data. In this paper, we also discuss the complexity of algorithms, allowing us to evaluate HMAP formulas, and show that the formulas can be expressed as constraint networks falling into the class of simple temporal problems, which can be solved in polynomial time.
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paper_title: Scenario recognition for temporal reasoning in medical domains

paper_content:

The recognition of high level clinical scenes is fundamental in patient monitoring. In this paper, we propose a technique for recognizing a session, i.e. the clinical process evolution, by comparison against a predetermined set of scenarios, i.e. the possible behaviors for this process. We use temporal constraint networks to represent both scenario and session. Specific operations on networks are then applied to perform the recognition task. An index of temporal proximity is introduced to quantify the degree of matching between two temporal networks in order to select the best scenario fitting a session. We explore the application of our technique, implemented in the Deja Vu system, to the recognition of typical medical scenarios with both precise and imprecise temporal information.
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paper_title: Extensions to the time-oriented database model to support temporal reasoning in medical expert systems

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Extensions to the time-oriented database model to support temporal reasoning in medical expert systems. -
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paper_title: Verification of temporal scheduling constraints in clinical practice guidelines

paper_content:

The computerization of clinical practice guidelines is a significant scientific challenge for the medical informatics community. One frequently reported factor hindering this objective is the existence of deficiencies within guideline knowledge. In this paper, we focus on the detection of flaws within temporal scheduling constraints. Temporal scheduling constraints are important elements of therapy management, and are frequently incorporated in clinical practice guidelines. We present a suitable verification method that is based on calculating the minimal network of temporal constraints on the execution of guideline activities. Our method serves three purposes: (1) it checks whether temporal scheduling constraints are consistent with scheduling constraints implied by control flow operators and the hierarchical structuring of a guideline; (2) it yields suggestions for an equivalent, yet more explicit representation of non-minimal constraints; (3) it can be used by the guideline interpreter to assemble feasible time intervals for the execution of each guideline activity. We evaluate our approach by applying it to a guideline specified in the Asbru language. For this purpose, we implemented a prototype verifier. Although we concentrate on the guideline representation language Asbru as the demonstration medium of our method within this paper, our approach can be reused to verify several alternative guideline representation formats.
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paper_title: Temporal data base management

paper_content:

Abstract Reasoning about time typically involves drawing conclusions on the basis of incomplete information. Uncertainty arises in the form of ignorance, indeterminacy, and indecision. Despite the lack of complete information, a problem solver is continually forced to make predictions in order to pursue hypotheses and plan for the future. Such predictions are frequently contravened by subsequent evidence. This paper presents a computational approach to temporal reasoning that directly confronts these issues. The approach centers around techniques for managing a data base of assertions corresponding to the occurrence of events and the persistence of their effects over time. The resulting computational framework performs the temporal analog of (static) reason maintenance by keeping track of dependency information involving assumptions about the truth of facts spanning various intervals of time. The system described in this paper extends classical predicate-calculus data bases, such as those used by PROLOG, to deal with time in an efficient and natural manner.
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paper_title: Temporal Constraint Networks

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Abstract This paper extends network-based methods of constraint satisfaction to include continuous variables, thus providing a framework for processing temporal constraints. In this framework, called temporal constraint satisfaction problem (TCSP), variables represent time points and temporal information is represented by a set of unary and binary constraints, each specifying a set of permitted intervals. The unique feature of this framework lies in permitting the processing of metric information, namely, assessments of time differences between events. We present algorithms for performing the following reasoning tasks: finding all feasible times that a given event can occur, finding all possible relationships between two given events, and generating one or more scenarios consistent with the information provided. We distinguish between simple temporal problems (STPs) and general temporal problems, the former admitting at most one interval constraint on any pair of time points. We show that the STP, which subsumes the major part of Vilain and Kautz's point algebra, can be solved in polynomial time. For general TCSPs, we present a decomposition scheme that performs the three reasoning tasks considered, and introduce a variety of techniques for improving its efficiency. We also study the applicability of path consistency algorithms as preprocessing of temporal problems, demonstrate their termination and bound their complexities.
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paper_title: Temporal representation and reasoning in artificial intelligence: A review

paper_content:

The explicit representation and reasoning about time is an important problem in many areas of artificial intelligence. Over the last 10-15 years, it has been attracting the attention of many researchers. Several temporal reasoning systems, differing in design issues related to ontology of time, underlying temporal logic, temporal constraints used and algorithms employed, have been developed. In this survey, important representational issues which determine a temporal reasoning system are introduced. In particular, several important notions like change, causality, actions are described in terms of time. For each issue different choices available in the literature are discussed. The most influential approaches to temporal reasoning in artificial intelligence are analyzed in terms of these major representational issues.
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paper_title: Temporal Reasoning Based on Semi-Intervals

paper_content:

Abstract A generalization of Allen's interval-based approach to temporal reasoning is presented. The notion of ‘conceptual neighborhood’ of qualitative relations between events is central to the presented approach. Relations between semi-intervals rather than intervals are used as the basic units of knowledge. Semi-intervals correspond to temporal beginnings or endings of events. We demonstrate the advantages of reasoning on the basis of semi-intervals: (1) semi-intervals are rather natural entities both from a cognitive and from a computational point of view; (2) coarse knowledge can be processed directly; computational effort is saved; (3) incomplete knowledge about events can be fully exploited; (4) incomplete inferences made on the basis of complete knowledge can be used directly for further inference steps; (5) there is no trade-off in computational strength for the added flexibility and efficiency; (6) for a natural subset of Allen's algebra, global consistency can be guaranteed in polynomial time; (7) knowledge about relations between events can be represented much more compactly.
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paper_title: A temporal logic for reasoning about processes and plans

paper_content:

Much previous work in artificial intelligence has neglected representing time in all its complexity. In particular, it has neglected continuous change and the indeterminacy of the future. To rectify this, I have developed a first-order temporal logic, in which it is possible to name and prove things about facts, events, plans, and world histories. In particular, the logic provides analyses of causality, continuous change in quantities, the persistence of facts (the frame problem), and the relationship between tasks and actions. It may be possible to implement a temporal-inference machine based on this logic, which keeps track of several “maps” of a time line, one per possible history.
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paper_title: Concerted instant-interval temporal semantics. I, Temporal ontologies

paper_content:

The general problem of the relationship between instant-based and interval-based temporal semantics is studied. The paper is in two parts. In this first part we consider instant and interval temporal structures and specify conditions for their mutual definability
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paper_title: Maintaining knowledge about temporal intervals

paper_content:

An interval-based temporal logic is introduced, together with a computationally effective reasoning algorithm based on constraint propagation. This system is notable in offering a delicate balance between
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paper_title: Temporal data base management

paper_content:

Abstract Reasoning about time typically involves drawing conclusions on the basis of incomplete information. Uncertainty arises in the form of ignorance, indeterminacy, and indecision. Despite the lack of complete information, a problem solver is continually forced to make predictions in order to pursue hypotheses and plan for the future. Such predictions are frequently contravened by subsequent evidence. This paper presents a computational approach to temporal reasoning that directly confronts these issues. The approach centers around techniques for managing a data base of assertions corresponding to the occurrence of events and the persistence of their effects over time. The resulting computational framework performs the temporal analog of (static) reason maintenance by keeping track of dependency information involving assumptions about the truth of facts spanning various intervals of time. The system described in this paper extends classical predicate-calculus data bases, such as those used by PROLOG, to deal with time in an efficient and natural manner.
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paper_title: Temporal representation and reasoning in artificial intelligence: A review

paper_content:

The explicit representation and reasoning about time is an important problem in many areas of artificial intelligence. Over the last 10-15 years, it has been attracting the attention of many researchers. Several temporal reasoning systems, differing in design issues related to ontology of time, underlying temporal logic, temporal constraints used and algorithms employed, have been developed. In this survey, important representational issues which determine a temporal reasoning system are introduced. In particular, several important notions like change, causality, actions are described in terms of time. For each issue different choices available in the literature are discussed. The most influential approaches to temporal reasoning in artificial intelligence are analyzed in terms of these major representational issues.
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paper_title: Temporal Reasoning and Temporal Data Maintenance in Medicine: Issues and Challenges

paper_content:

Abstract We present a brief, nonexhaustive overview of research efforts in designing and developing time-oriented systems in medieine. The growing volume of research on time-oriented systems in medicine can be viewed from either an application point of view, focusing on different generic tasks (e.g. diagnosis) and clinical areas (e.g. cardiology), or from a methodological point of view, distinguishing between different theoretical approaches. In this overview, we focus on highlighting methodological and theoretical choices, and conclude with suggestions for new research directions. Two main research directions can be noted: temporal reasoning, which supports various temporal inference tasks (e.g. temporal abstraction, time-oriented decision support, forecasting, data validation), and temporal data maintenance, which deals with storage and retrieval of data that have heterogeneous temporal dimensions. Efforts common to both research areas include the modeling of time, of temporal entities, and of temporal queries. We suggest that tasks such as abstraction of time-oriented data and the handling of different temporal-granularity levels should provide common ground for collaboration between the two research directions and fruitful areas for future research.
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paper_title: Temporal representation of clinical algorithms using expert-system and database tools

paper_content:

The HyperLipid Advisory System combines a rule-based implementation of a clinical algorithm (the NIH Cholesterol Education Program Expert Panel recommendations) with a temporal representation that facilitates reasoning over time while maintaining efficient storage in a standard database. The temporal representation consists of objects that model point events such as visits and interval events such as specific therapies. These objects are combined into abstractions called phases, which correspond to higher level clinical concepts such as a diet or drug treatment. The time-oriented data objects are referenced in the rules using a custom-tailored operator query language. Between user sessions relevant clinical data are stored in external files. When the advisory system is reconsulted, this information is retrieved and mapped back into an object-oriented format. Use of a commercially available expert-system shell for such tasks allows algorithm implementation in standard personal computing environments.
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paper_title: Temporal reasoning for decision support in medicine

paper_content:

Objective: Handling time-related concepts is essential in medicine. During diagnosis it can make a substantial difference to know the temporal order in which some symptoms occurred or for how long they lasted. During prognosis the potential evolutions of a disease are conceived as a description of events unfolding in time. In therapy planning the different steps of treatment must be applied in a precise order, with a given frequency and for a certain span of time in order to be effective. This article offers a survey on the use of temporal reasoning for decision support-related tasks in medicine.Material and methods: Key publications of the area, mainly circumscribed to the latest two decades, are reviewed and classified according to three important stages of patient treatment requiring decision support: diagnosis, prognosis and therapy planning/management. Other complementary publications, like those on time-centered information storage and retrieval, are also considered as they provide valuable support to the above mentioned three stages.Results: Key areas are highlighted and used to organize the latest contributions. The survey of previous research is followed by an analysis of what can still be improved and what is needed to make the next generation of decision support systems for medicine more effective.Conclusions: It can be observed that although the area has been considerably developed, there are still areas where more research is needed to make time-based systems of widespread use in decision support-related areas of medicine. Several suggestions for further exploration are proposed as a result of the survey.
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paper_title: A temporal query system for protocol-directed decision support.

paper_content:

Chronus is a query system that supports temporal extensions to the Structured Query Language (SQL) for relational databases. Although the relational data model can store time-stamped data and can permit simple temporal-comparison operations, it does not provide either a closed or a sufficient algebra for manipulating temporal data. In this paper, we outline an algebra that maintains a consistent relational representation of temporal data and that allows the type of temporal queries needed for protocol-directed decision support. We also discuss how Chronus can translate between our temporal algebra and the relational algebra used for SQL queries. We have applied our system to the task of screening patients for clinical trials. Our results demonstrate that Chronous can express sufficiently all required temporal queries, and that the search time of such queries is similar to that of standard SQL.
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paper_title: Model Formulation: Modeling Electronic Discharge Summaries as a Simple Temporal Constraint Satisfaction Problem

paper_content:

OBJECTIVE ::: To model the temporal information contained in medical narrative reports as a simple temporal constraint satisfaction problem. ::: ::: ::: DESIGN ::: A constraint satisfaction problem is defined by time points and constraints (inequalities between points). A time interval comprises a pair of points and a constraint. Five complete electronic discharge summaries and paragraphs from 226 other discharge summaries were studied. Medical events were represented as intervals, and assertions about events were represented as constraints. Through a consensus process, a set of encoding procedures and a list of issues related to encoding were generated. ::: ::: ::: MEASUREMENTS ::: Instances of temporal disjunction and contradiction and distribution of temporal constraints were used. ::: ::: ::: RESULTS ::: An average of 95 medical events (range, 46-151) and 234 temporal assertions (range, 118-388) were identified per complete discharge summary. Nondefinitional assertions were explicit (36%) or implicit (64%) and absolute (17%), qualitative (72%), or metric (11%). Implicit assertions were based on domain knowledge and assumptions, e.g., the section of the report determined the ordering of events. Issues included linking events, intermittence, periodicity, granularity, vagueness, ambiguity, uncertainty, and plans. ions such as intermittence were not represented explicitly. The temporal network was sparse: Only 0.80% (range, 0.42%-1.38%) of possible constraints were instantiated. No instances of discontinuous temporal disjunction were found in the complete summaries or the 226 paragraphs. One instance of temporal contradiction was found (intrareport rate of 0.2 with a 95% confidence interval of 0.005-1.114). ::: ::: ::: CONCLUSION ::: A simple temporal constraint satisfaction problem appears sufficient to represent most temporal assertions in discharge summaries and may be useful for encoding electronic medical records.
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paper_title: A Critical Examination of Allen's Theory of Action and Time

paper_content:

Abstract J.F. Allen's theory of time and action is examined and found to be unsuitable for representing facts about continuous change. A series of revisions to Allen's theory is proposed in order to accommodate this possibility. The principal revision is a diversification of the temporal ontology to include instants on the same footing as intervals; a distinction is also made between two kinds of property, called states of position and states of motion, with respect to the logic of their temporal incidence. As a consequence of these revisions, it is also found necessary to diversify the range of predicates specifying temporal location. Finally, it is argued that Allen's category of processes is superfluous, since it can be assimilated with the category of properties. The implications of this assimilation for the representation of sentences containing verbs in the progressive aspect are discussed.
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paper_title: A prognostic model for temporal courses that combines temporal abstraction and case-based reasoning.

paper_content:

Since clinical management of patients and clinical research are essentially time-oriented endeavours, reasoning about time has become a hot topic in medical informatics. Here we present a method for prognosis of temporal courses, which combines temporal abstractions with case-based reasoning. It is useful for application domains where neither well-known standards, nor known periodicity, nor a complete domain theory exist. We have used our method in two prognostic applications. The first one deals with prognosis of the kidney function for intensive care patients. The idea is to elicit impairments on time, especially to warn against threatening kidney failures. Our second application deals with a completely different domain, namely geographical medicine. Its intention is to compute early warnings against approaching infectious diseases, which are characterised by irregular cyclic occurrences. So far, we have applied our program on influenza and bronchitis. In this paper, we focus on influenza forecast and show first experimental results.
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paper_title: Medical multiparametric time course prognoses applied to kidney function assessments

paper_content:

In this paper, we describe an approach to utilize case-based reasoning methods for trend prognoses for the monitoring of the kidney function in an Intensive Care Unit (ICU) setting. Since using conventional methods for reasoning over time does not fit for course predictions with poor medical knowledge of typical course patterns, we have developed abstraction methods suitable for integration into our case-based reasoning system ICONS. These methods combine medical experience with prognoses of multiparametric courses. On the ICU, the monitoring system NIMON provides a daily report based on current measured and calculated kidney function parameters. Subsequently, we generate course-characteristic trend descriptions of the renal function over the course of time. Using case-based reasoning retrieval methods, we search in the case base for courses similar to the current trend descriptions. Finally, we present the current course together with similar courses as comparisons and as probable prognoses to the user. We applied case-based reasoning methods in a domain which seemed reserved for statistical methods and conventional temporal reasoning.
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paper_title: Representing and developing temporally abstracted knowledge as a means towards facilitating time modeling in medical decision-support systems.

paper_content:

Abstract The utilization of the appropriate level of temporal abstraction is an important aspect of time modeling. We discuss some aspects of the relation of temporal abstraction to important knowledge engineering parameters such as model correctness, case of model specification, knowledge availability, query completeness, inference tractability, and semantic clarity. We propose that versatile and efficient time-modeling formalisms should encompass ways to represent and reason at more than one level of abstraction, and we discuss such a hybrid formalism. Although many research efforts have concentrated on the automation of specific temporal abstractions, much research needs to be done in understanding and developing provably optimal abstractions. We provide an initial framework for studying this problem in a manner that is independent of the particular problem domain and knowledge representation, and suggest several research challenges that appear worth pursuing.
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paper_title: Managing temporal worlds for medical trend diagnosis

paper_content:

Abstract The medical trend diagnosis system TrenDx has been applied as a prototype for diagnosing pediatric growth disorders, and as a proof of concept in detecting clinically significant trends in hemodynamics and blood gases in intensive care unit patients. TrenDx diagnoses trends by matching patient data to patterns of normal and abnormal trends called trend templates that define disorders as typical patterns of relevant variables. These patterns consist of a partially ordered set of temporal intervals with uncertain endpoints. Bound to each temporal interval are value constraints on real-valued functions of measurable parameters. The temporal uncertainty in trend templates allows TrenDx to conclude both what trend pattern best matches the data and also when significant landmarks and phase transitions have occurred within the best matching trend. The temporal uncertainty in trend templates requires that TrenDx consider alternate temporal worlds in monitoring patient data. The number of temporal worlds grows worst case polynomially in the number of time slices of data. To manage the competing temporal worlds, TrenDx employs two techniques: beam search based on regression scores, and temporal granularity in the trend template definitions. These two techniques, described here in detail, allow TrenDx to choose different points in the trade-off between accuracy of trend detection and algorithm efficiency.
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paper_title: Development of a knowledge base for diagnostic reasoning in cardiology

paper_content:

This paper reports on a formative evaluation of the diagnostic capabilities of the Heart Failure Program, which uses a probability network and a heuristic hypothesis generator. Using 242 cardiac cases collected from discharge summaries at a tertiary care hospital, we compared the diagnoses of the program to diagnoses collected from cardiologists using the same information as was available to the program. With some adjustments to the knowledge base, the Heart Failure Program produces appropriate diagnoses about 90% of the time on this training set. The main reasons for the inappropriate diagnoses of the remaining 10% include inadequate reasoning with temporal relations between cause and effect, severity relations, and independence of acute and chronic diseases.
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paper_title: Merging multimedia presentations and semistructured temporal data: a graph-based model and its application to clinical information

paper_content:

Objective:: In this paper, we focus on the issue of providing physicians with the capability of representing in a seamless way both temporal aspects of multimedia semistructured data and their temporal presentation requirements. Background:: Semistructured data are data having some structure, that may be irregular or incomplete and does not necessarily conform to a fixed schema. Semistructured data often contain the description of histories of the considered real world. The eXtensible Markup Language (XML) is becoming a cross compatible and standardized means for representing semistructured clinical data. In the field of medical informatics, there are many ongoing activities concerning XML. In the field of multimedia database systems, the topic related to the integration of several media objects (with their temporal aspects) have been considered both for data modeling and querying issues and for modeling multimedia presentations. Methodology:: We first propose the Multimedia Temporal Graphical Model (MTGM), by representing a clinical database for cardiology patients undergoing cardiac angiographies and then describe it in a formal way. We deal with the problem of expressing MTGM data by XML and of managing MTGM clinical data through an XML-based system. We provide both a technique for translating (a part of) an MTGM database into an XML document and some techniques allowing us to obtain presentations defined by means of the Synchronized Multimedia Integration Language (SMIL) from MTGM presentations. Results:: MTGM allows one to represent and store clinical information in a semistructured, temporal, and multimedia database. The physician can define multimedia presentations based on the stored data. Multimedia presentations are then stored in the same MTGM database together with temporal clinical information and are thus represented according to the same data model. A prototype based on an XML native database system has been designed and implemented. Discussion and conclusions:: In this work we have considered the theoretical and methodological issues concerning the definition of a general data model for describing temporal and multimedia features of semistructured clinical information. Other research and application oriented features, which have not been considered in MTGM, could be investigated for completing MTGM with regard to its applicability to clinical domains: MTGM does not allow one to express times at different levels of granularities, i.e. with different time units, or with indeterminacy; besides the considered valid time, it could be interesting to manage also other temporal dimensions such as the transaction and availability times. Besides being useful for managing multimedia data stored according to widely accepted standards as MPEG and DICOM, nowadays semistructured data, and XML in particular, are becoming the most important way for expressing and exchanging medical knowledge and data: MTGM can be considered as a data model allowing the seamless representation of both (multimedia and temporal) clinical data and knowledge.
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paper_title: The EON model of intervention protocols and guidelines.

paper_content:

Abstract ::: We present a computational model of treatment protocols abstracted from implemented systems that we have developed previously. In our framework, a protocol is modeled as a hierarchical plan where high-level protocol steps are decomposed into descriptions of more specific actions. The clinical algorithms embodied in a protocol are represented by procedures that encode the sequencing, looping, and synchronization of protocol steps. The representation allows concurrent and optional protocol steps. We define the semantics of a procedure in terms of an execution model that specifies how the procedure should be interpreted. We show that the model can be applied to an asthma guideline different from the protocols for which the model was originally constructed.
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paper_title: Knowledge-based temporal abstraction in diabetes therapy.

paper_content:

We suggest a general framework for solving the task of creating abstract, interval-based concepts from time-stamped clinical data. We refer to this problem-solving framework as the knowledge-based temporal-abstraction (KBTA) method. The KBTA method emphasizes explicit representation, acquisition, maintenance, reuse, and the sharing of knowledge required for abstraction of time-oriented clinical data. We describe the subtasks into which the KBTA method decomposes its task, the problem-solving mechanisms that solve these subtasks, and the knowledge necessary for instantiating these mechanisms in a particular clinical domain. We have implemented the KBTA method in the RESUME system and have applied it to the task of monitoring the care of insulin-dependent diabetics.
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paper_title: Combining physiologic models and symbolic methods to interpret time-varying patient data.

paper_content:

This paper describes a methodology for representing and using medical knowledge about temporal relationships to infer the presence of clinical events that evolve over time. The methodology consists of three steps: (1) the incorporation of patient observations into a generic physiologic model, (2) the conversion of model states and predictions into domain-specific temporal abstractions, and (3) the transformation of temporal abstractions into clinically meaningful descriptive text. The first step converts raw observations to underlying model concepts, the second step identifies temporal features of the fitted model that have clinical interest, and the third step replaces features represented by model parameters and predictions into concepts expressed in clinical language. We describe a program, called TOPAZ, that uses this three-step methodology. TOPAZ generates a narrative summary of the temporal events found in the electronic medical record of patients receiving cancer chemotherapy. A unique feature of TOPAZ is its use of numeric and symbolic techniques to perform different temporal reasoning tasks. Time is represented both as a continuous process and as a set of temporal intervals. These two temporal models differ in the temporal ontology they assume and in the temporal concepts they encode. Without multiple temporal models, this diversity of temporal knowledge could not be represented.
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paper_title: A framework for knowledge-based temporal abstraction

paper_content:

A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events and contexts. A formal specification of a domain's temporal abstraction knowledge supports acquisition, maintenance, reuse and sharing of that knowledge. ::: ::: The knowledge-based temporal abstraction method decomposes the temporal abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal abstraction mechanisms. The temporal abstraction mechanisms depend on four domain-specific knowledge types: structural, classification (functional), temporal semantic (logical) and temporal dynamic (probabilistic) knowledge. Domain values for all knowledge types are specified when a temporal abstraction system is developed. ::: ::: The knowledge-based temporal abstraction method has been implemented in the RESUME system and has been evaluated in several clinical domains (protocol-based care, monitoring of children's growth and therapy of diabetes) and in an engineering domain (monitoring of traffic control), with encouraging results.
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paper_title: Temporal Reasoning and Temporal Data Maintenance in Medicine: Issues and Challenges

paper_content:

Abstract We present a brief, nonexhaustive overview of research efforts in designing and developing time-oriented systems in medieine. The growing volume of research on time-oriented systems in medicine can be viewed from either an application point of view, focusing on different generic tasks (e.g. diagnosis) and clinical areas (e.g. cardiology), or from a methodological point of view, distinguishing between different theoretical approaches. In this overview, we focus on highlighting methodological and theoretical choices, and conclude with suggestions for new research directions. Two main research directions can be noted: temporal reasoning, which supports various temporal inference tasks (e.g. temporal abstraction, time-oriented decision support, forecasting, data validation), and temporal data maintenance, which deals with storage and retrieval of data that have heterogeneous temporal dimensions. Efforts common to both research areas include the modeling of time, of temporal entities, and of temporal queries. We suggest that tasks such as abstraction of time-oriented data and the handling of different temporal-granularity levels should provide common ground for collaboration between the two research directions and fruitful areas for future research.
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paper_title: EON: A Component-Based Approach to Automation of Protocol-Directed Therapy

paper_content:

P rovision of, automated support for planning protocol-directed therapy requires a computer program to take as input clinical data stored in an electronic patient-record system and to generate as output recommendations for therapeutic interventions and laboratory testing that are defined by applicable protocols. This paper presents a synthesis of research carried out at Stanford University to model the therapy-planning task and to demonstrate a component-based architecture for building protocol-based decision-support systems. We have constructed general-purpose software components that (1) interpret abstract protocol specifications to construct appropriate patient-specific treatment plans; (2) infer from time- stamped patient data higher-level, interval-based, abstract concepts; (3) perform time-oriented queries on a time-oriented patient database; and (4) allow acquisition and maintenance of protocol knowledge in a manner that facilitates efficient processing both by humans and by computers. We have implemented these components in a computer system known as EON. Each of the components has been developed, evaluated, and reported independently. We have evaluated the integration of the components as a composite architecture by implementing T-HELPER, a computer-based patient-record system that uses EON to offer advice regarding the management of patients who are following clinical trial protocols for AIDS or HIV infection. A test of the reuse of the software components in a different clinical domain demonstrated rapid development of a prototype application to support protocol-based care of patients who have breast cancer.
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paper_title: Diagnostic Patterns and Temporal Trends in the Evaluation of Adult Patients Hospitalized With Syncope

paper_content:

BACKGROUND ::: Syncope is a common clinical problem that is often difficult and expensive to diagnose. We examined diagnostic patterns and trends and use of specialty consultations in the evaluation of syncope. ::: ::: ::: METHODS ::: We retrospectively reviewed the medical records of consecutive adult patients hospitalized with the principal diagnosis of syncope (International Classification of Diseases, Ninth Revision, code 780.2) during 1994 and 1998 at 2 community teaching hospitals. ::: ::: ::: RESULTS ::: A total of 649 patients (57% female) with a mean (+/-SD) age of 68 +/- 15 years were identified in 1994 (n = 451) and 1998 (n = 198). Three hundred forty-one patients (53%) underwent at least 1 neurologic test, including brain computed tomographic (CT) scan (n = 283), electroencephalography (n = 253), carotid Doppler echocardiography (n = 185), and brain magnetic resonance imaging (n = 10). Only brain CT scan and electroencephalography yielded diagnoses in 5 (2%) and 6 patients (2%), respectively with history consistent with seizures or stroke. Cardiovascular tests providing the highest diagnostic yields (postural blood pressure check in 52 [30%], head-up tilt-table test in 32 [24%], and electrophysiologic study in 5 [16%]) were used in 176 (27%), 132 (20%), and 31 patients (5%), respectively. Differences in the use of some tests were noted at the participating hospitals and over time (1994 vs 1998). The total number of diagnosed cases was similar for patients undergoing evaluation by primary care physicians alone (65/103 [63%]), compared with cardiology (48/85 [56%]), neurology (29/48 [60%]), or both (81/141 [57%]). After a mean (+/-SD) length of stay of 5 +/- 4 days, 320 (49%) of 649 cases remained undiagnosed. ::: ::: ::: CONCLUSIONS ::: Despite a reduction in the use of some tests (eg, brain CT scan and carotid Doppler) over time, lower-yield neurologic tests were overused and higher-yield cardiovascular tests were likely underused. The untargeted, seemingly random use of specialty evaluations did not seem to contribute to an increase in the overall number of diagnosed cases. Increased use of specific tests directed by history and results of physical examination may improve diagnostic yield and decrease the cost of evaluating syncope.
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paper_title: Model-based interpretation of the ECG: a methodology for temporal and spatial reasoning

paper_content:

Abstract A new software architecture for automatic interpretation of the electrocardiographic rhythm is presented. Using the hypothesize-and-test paradigm, a semiquantitative physiological model and production rule-based knowledge are combined to reason about time- and space-varying characteristics or complex heart rhythms. A prototype system implementing the methodology accepts semiquantitative description of the onset and morphology of the P waves and QRS complexes that are observed in the body-surface electrocardiogram. A beat-by-beat explanation of the origin and consequences of each wave is produced. The output is in the standard cardiology laddergram. The current prototype generates the full differential diagnosis of narrow-complex tachycardia and correctly diagnoses complex rhythms, such as atrioventricular (AV) nodal reentrant tachycardia with either hidden or visible P waves and varying degrees of AV block.
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paper_title: RASTA: A Distributed Temporal Abstraction System to facilitate Knowledge-Driven Monitoring of Clinical Databases

paper_content:

The time dimension is very important for applications that reason with clinical data. Unfortunately, this task is inherently computationally expensive. As clinical decision support systems tackle increasingly varied problems, they will increase the demands on the temporal reasoning component, which may lead to slow response times. This paper addresses this problem. It describes a temporal reasoning system called RASTA that uses a distributed algorithm that enables it to deal with large data sets. The algorithm also supports a variety of configuration options, enabling RASTA to deal with a range of application requirements.
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paper_title: Model-based interpretation of the ECG: a methodology for temporal and spatial reasoning.

paper_content:

A new software architecture for automatic interpretation of the electrocardiographic rhythm is presented. Using the hypothesize-and-test paradigm, a semiquantitative physiological model and production rule-based knowledge are combined to reason about time- and space-varying characteristics of complex heart rhythms. A prototype system implementing the methodology accepts a semiquantitative description of the onset and morphology of the P waves and QRS complexes that are observed in the body-surface electrocardiogram. A beat-by-beat explanation of the origin and consequences of each wave is produced. The output is in the standard cardiology laddergram format. The current prototype generates the full differential diagnosis of narrow-complex tachycardia and correctly diagnoses complex rhythms, such as atrioventricular (AV) nodal reentrant tachycardia with either hidden or visible P waves and varying degrees of AV block.
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paper_title: A Temporal Analysis of QMR

paper_content:

OBJECTIVE ::: To understand better the trade-offs of not incorporating explicit time in Quick Medical Reference (QMR), a diagnostic system in the domain of general internal medicine, along the dimensions of expressive power and diagnostic accuracy. ::: ::: ::: DESIGN ::: The study was conducted in two phases. Phase I was a descriptive analysis of the temporal abstractions incorporated in QMR's terms. Phase II was a pseudo-prospective controlled experiment, measuring the effect of history and physical examination temporal content on the diagnostic accuracy of QMR. ::: ::: ::: MEASUREMENTS ::: For each QMR finding that would fit our operational definition of temporal finding, several parameters describing the temporal nature of the finding were assessed, the most important ones being: temporal primitives, time units, temporal uncertainty, processes, and patterns. The history, physical examination, and initial laboratory results of 105 consecutive patients admitted to the Pittsburgh University Presbyterian Hospital were analyzed for temporal content and factors that could potentially influence diagnostic accuracy (these included: rareness of primary diagnosis, case length, uncertainty, spatial/causal information, and multiple diseases). ::: ::: ::: RESULTS ::: 776 findings were identified as temporal. The authors developed an ontology describing the terms utilized by QMR developers to express temporal knowledge. The authors classified the temporal abstractions found in QMR in 116 temporal types, 11 temporal templates, and a temporal hierarchy. The odds of QMR's making a correct diagnosis in high temporal complexity cases is 0.7 the odds when the temporal complexity is lower, but this result is not statistically significant (95% confidence interval = 0.27-1.83). ::: ::: ::: CONCLUSIONS ::: QMR contains extensive implicit time modeling. These results support the conclusion that the abstracted encoding of time in the medical knowledge of QMR does not induce a diagnostic performance penalty.
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paper_title: Computer aided fuzzy medical diagnosis

paper_content:

This paper describes a fuzzy approach to computer aided medical diagnosis in a clinical context. It introduces a formal view of diagnosis in clinical settings and shows the relevance and possible uses of fuzzy cognitive maps and fuzzy logic. A constraint satisfaction method is introduced which uses the temporal uncertainty in symptom durations that may occur with particular diseases. Together with fuzzy symptom descriptions, the method results in an estimate of the stage of a disease if the temporal constraints of the disease in relation to the occurrence of the symptoms are satisfied. The approach is evaluated through simulation experiments showing the effects of symptom ordering, temporal uncertainty and symptom strengths on the diagnosis efficiency. The method is effective and can be developed further using second order (Type 2) fuzzy logic to better represent uncertainty in the clinical context thus improving differential diagnosis accuracy.
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paper_title: A temporal representation and reasoning model for medical decision-support systems

paper_content:

Reasoning with temporal concepts is an essential part of medical problem solving. As a result, medical decision-support systems need effective formalisms (i.e., representation and reasoning languages) for time modeling. ::: Time modeling has been extensively studied in many disciplines. Consequently, we know today methods for handling important aspects of time modeling, as well as a number of unsolved conceptual and engineering challenges. On the basis of previous research, this thesis proposes a set of desiderata for formalisms for medical time modeling. ::: The dissertation addresses 3 hypotheses: It is possible to construct a formalism that meets the time-modeling desiderata (hypothesis #1). It is possible to apply the new formalism to a medical domain of high temporal complexity and demonstrate expressivity (hypothesis #2) and tractability (hypothesis #3) advantages over alternative established formalisms. ::: Current formalisms for temporal modeling achieve tractability by significantly constraining the temporal representations they use and the inferences they can make, or by abolishing formal temporal semantics. Since high expressiveness and explicitness are part of the time-modeling desiderata, this work adopts a different strategy, based on two conceptual principles: Hybrid temporal abstraction/explicitness allows the simultaneous representation of different levels of temporal abstraction and explicitness in the same model. This flexibility prevents unnecessary model size growth. Dynamic temporal abstraction constructs a small/explicit (and relatively more tractable) model that is pertinent to a query or patient from a large/implicit (and relatively less tractable) model that is pertinent to a domain or population. ::: Using the above principles, Bayesian belief networks are extended to represent time and causality. The new formalism is called Modifiable Temporal Belief Networks (MTBNs). Formal definitions, properties, inference procedures and modeling techniques are given for MTBNs. It is shown that MTBNs address the desiderata for time modeling in medical decision-support systems. An initial formative study of MTBNs is conducted by applying them in the domain of liver transplantation. The liver modeling experiment suggests that MTBNs have expressivity and tractability advantages over alternative formalisms. This work concludes by discussing current weaknesses of MTBNs and corresponding research problems.
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paper_title: Predicting Patient’s Long-Term Clinical Status after Hip Arthroplasty Using Hierarchical Decision Modelling and Data Mining

paper_content:

Construction of a prognostic model is presented for the longterm outcome after femoral neck fracture treatment with implantation of hip endoprosthesis. While the model is induced from the follow-up data, we show that the use of additional expert knowledge is absolutely crucial to obtain good predictive accuracy. A schema is proposed where domain knowledge is encoded as a hierarchical decision model of which only a part is induced from the data while the rest is specified by the expert. Although applied to hip endoprosthesis domain, the proposed schema is general and can be used for the construction of other prognostic models where both follow-up data and human expertise is available.
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paper_title: Spatio-temporal reasoning for multi-scale modeling in cardiology

paper_content:

Abstract In this paper, we present an overview of the CARDIOLAB environment where the heart's electrical activity is modeled at distinct space and time scales. CARDIOLAB uses three models, two of which are based on cellular automata, and the third on qualitative simulation. They are combined around a blackboard for multi-scale quantitative and qualitative modeling of cardiac electrical activity. A general account on how spatio-temporal representation and reasoning methods are applied to produce heuristic associations is also presented.
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paper_title: Incorporating temporal and clinical reasoning in a new measure of continuity of care.

paper_content:

Abstract ::: Previously described quantitative methods for measuring continuity of care have assumed that perfect continuity exists when a patient sees only one provider, regardless of the temporal pattern and clinical context of the visits. This paper describes an implementation of a new operational model of continuity--the Temporal Continuity Index--that takes into account time intervals between well visits in a pediatric residency continuity clinic. Ideal continuity in this model is achieved when intervals between visits are appropriate based on the age of the patient and clinical context of the encounters. The fundamental concept in this model is the expectation interval, which contains the length of the maximum ideal follow-up interval for a visit and the maximum follow-up interval. This paper describes an initial implementation of the TCI model and compares TCI calculations to previous quantitative methods and proposes its use as part of the assessment of resident education in outpatient settings.
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paper_title: MAGIC: An experimental system for generating multimedia briefings about post-bypass patient status

paper_content:

We describe MAGIC, an experimental system for generating multimedia briefings about the clinical status of post-bypass patients entering a cardiac ICU. MAGIC is a distributed system whose components use knowledge-based techniques for planning and generating briefings in text, speech, and graphics. These briefings are coordinated together by reasoning with dynamically generated temporal and spatial constraints. Formative evaluation using system mock-ups with ICU nurses and residents have been used to determine the general format and content of these briefings. We present an overview of MAGIC's architecture and show what it can currently generate.
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paper_title: Prognoses of Multiparametric Medical Time Courses Applied to Kidney Function Assessments

paper_content:

In this paper, we describe an approach to utilize Case-Based Reasoning methods for trend prognoses for the monitoring of the kidney function in an Intensive Care Unit (ICU) setting. Since using conventional methods for reasoning over time does not fit for course predictions with poor medical knowledge of typical course patterns, we have developed abstraction methods suitable for integration into our Case-Based Reasoning system ICONS. These methods combine medical experience with prognoses of multiparametric courses. On the ICU, the monitoring system NIMON provides a daily report based on current measured and calculated kidney function parameters. We subsequently generate course-characteristic trend descriptions of the renal function over the course of time. Using Case-Based Reasoning retrieval methods, we search in the case base for courses similar to the current trend descriptions. Finally, we present the current course together with similar courses as comparisons and as possible prognoses to the user. We applied Case-Based Reasoning methods in a domain which seemed reserved for statistical methods and conventional temporal reasoning.
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paper_title: Cognitive Evaluation of an Innovative Psychiatric Clinical Knowledge Enhancement System

paper_content:

Psychiatric Clinical Knowledge Enhancement System (PSYCKES) is an innovative information system that presents patient medication history in tabular and graphical form. The system is designed to support therapeutic decision making. In this paper, we present a multifaceted cognitive evaluation of this system. The evaluation includes a cognitive walkthrough which is a task-analytic method for usability evaluation. We also conducted cognitive studies of two trainee and two attending psychiatrists using the system. One of the attending subjects is presented as a case study. An objective of this research is to characterize the way PSYCKES mediates reasoning. The study found that clinicians were able to use the system effectively to extract and coordinate information and draw appropriate inferences. The expert clinicians were better able to construct a coherent patient representation. The study also documented a few usability problems pertaining to the temporal integration of patient data. PSYCKES is a multifaceted tool that can significantly enhance therapeutic decision making.
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paper_title: Time series prediction using belief network models

paper_content:

Abstract We address the problem of generating normative forecasts efficiently from a Bayesian belief network. Forecasts are predictions of future values of domain variables conditioned on current and past values of domain variables. To address the forecasting problem, we have developed a probability forecasting methodology, Dynamic Network Models (DNMs), through a synthesis of belief network models and classical time-series models. The DNM methodology is based on the integration of fundamental methods of Bayesian time-series analysis, with recent additive generalizations of belief network representation and inference techniques. We apply DNMs to the problem of forecasting episodes of apnea, that is, regular intervals of breathing cessation in patients afflicted with sleep apnea. We compare the one-step-ahead forecasts of chest volume, an indicator of apnea, made by autoregressive models, belief networks, and DNMs. We also construct a DNM to analyse the multivariate time series of chest volume, heart rate and oxygen saturation data.
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paper_title: Temporal reasoning for decision support in medicine

paper_content:

Objective: Handling time-related concepts is essential in medicine. During diagnosis it can make a substantial difference to know the temporal order in which some symptoms occurred or for how long they lasted. During prognosis the potential evolutions of a disease are conceived as a description of events unfolding in time. In therapy planning the different steps of treatment must be applied in a precise order, with a given frequency and for a certain span of time in order to be effective. This article offers a survey on the use of temporal reasoning for decision support-related tasks in medicine.Material and methods: Key publications of the area, mainly circumscribed to the latest two decades, are reviewed and classified according to three important stages of patient treatment requiring decision support: diagnosis, prognosis and therapy planning/management. Other complementary publications, like those on time-centered information storage and retrieval, are also considered as they provide valuable support to the above mentioned three stages.Results: Key areas are highlighted and used to organize the latest contributions. The survey of previous research is followed by an analysis of what can still be improved and what is needed to make the next generation of decision support systems for medicine more effective.Conclusions: It can be observed that although the area has been considerably developed, there are still areas where more research is needed to make time-based systems of widespread use in decision support-related areas of medicine. Several suggestions for further exploration are proposed as a result of the survey.
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paper_title: NasoNet, Modeling the Spread of Nasopharyngeal Cancer with Networks of Probabilistic Events in Discrete Time

paper_content:

The spread of cancer is a non-deterministic dynamic process. As a consequence, the design of an assistant system for the diagnosis and prognosis of the extent of a cancer should be based on a representation method that deals with both uncertainty and time. The ultimate goal is to know the stage of development of a cancer in a patient before selecting the appropriate treatment. A network of probabilistic events in discrete time (NPEDT) is a type of Bayesian network for temporal reasoning that models the causal mechanisms associated with the time evolution of a process. This paper describes NasoNet, a system that applies NPEDTs to the diagnosis and prognosis of nasopharyngeal cancer. We have made use of temporal noisy gates to model the dynamic causal interactions that take place in the domain. The methodology we describe is general enough to be applied to any other type of cancer.
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paper_title: Model-based interpretation of time-ordered medical data

paper_content:

Time-ordered data often contain implications that physicians infer only by recognizing subtle temporal patterns. This dissertation is concerned with representing and using medical knowledge about temporal relationships so that decision-support programs can deduce abstract clinical concepts that appear as temporal patterns in time-ordered clinical observations. ::: Computational models of time that are based on a single representational method are limited by the expressive power of the chosen method. I propose that combining multiple temporal formalisms facilitates the creation of robust temporal models. The use of multiple modeling techniques permits each method to represent those temporal relationships that are best expressed within a particular formalism. ::: This dissertation proposes a methodology for interpreting time-ordered clinical data using a combination of numeric and symbolic techniques. The methodology proposes three transformations: (1) model-based transformation of time-ordered observations into underlying concepts, (2) interval-based transformation of dynamic model predictions into domain-specific temporal abstractions, and (3) state-based transformation of temporal abstractions into clinically meaningful discourse fragments. The temporal model used to convert observations into underlying system (patient) concepts encodes physiological process knowledge. The temporal model used to convert model-based predictions into temporal abstractions encodes domain-specific event knowledge. These two temporal models differ in the temporal structure they assume and in the temporal concepts they encode. Without multiple temporal models, this diversity of temporal knowledge could not be represented. ::: Effective medical decision making demands that physicians recognize and exploit the complex temporal changes that permeate the medical record. In this dissertation, I examine the temporal features of time-ordered clinical data in the context of summarizing a patient's medical record. I have demonstrated the three-transformation methodology in a computer program, call TOPAZ, that summarizes the clinical course of individual patients who are receiving experimental cancer chemotherapy.
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paper_title: Internist-I, an Experimental Computer-Based Diagnostic Consultant for General Internal Medicine

paper_content:

INTERNIST-I is an experimental computer program capable of making multiple and complex diagnoses in internal medicine. It differs from most other programs for computer-assisted diagnosis in the generality of its approach and the size and diversity of its knowledge base. To document the strengths and weaknesses of the program we performed a systematic evaluation of the capabilities of INTERNIST-I. Its performance on a series of 19 clinicopathological exercises (Case Records of the Massachusetts General Hospital) published in the Journal appeared qualitatively similar to that of the hospital clinicians but inferior to that of the case discussants. The evaluation demonstrated that the present form of the program is not sufficiently reliable for clinical applications. Specific deficiencies that must be overcome include the program’s inability to reason anatomically or temporally, its inability to construct differential diagnoses spanning multiple problem areas, its occasional attribution of findings to improper causes, and its inability to explain its “thinking.” (N Engl J Med. 1982; 307:468–76.
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paper_title: Scenario recognition for temporal reasoning in medical domains

paper_content:

The recognition of high level clinical scenes is fundamental in patient monitoring. In this paper, we propose a technique for recognizing a session, i.e. the clinical process evolution, by comparison against a predetermined set of scenarios, i.e. the possible behaviors for this process. We use temporal constraint networks to represent both scenario and session. Specific operations on networks are then applied to perform the recognition task. An index of temporal proximity is introduced to quantify the degree of matching between two temporal networks in order to select the best scenario fitting a session. We explore the application of our technique, implemented in the Deja Vu system, to the recognition of typical medical scenarios with both precise and imprecise temporal information.
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paper_title: Mining Biomedical Time Series By Combining Structural Analysis and Temporal Abstractions

paper_content:

Abstract ::: This paper describes the combination of Structural Time Series analysis and Temporal Abstractions for the interpretation of data coming from home monitoring of diabetic patients. Blood Glucose data are analyzed by a novel Bayesian technique for time series analysis. The results obtained are post-processed using Temporal Abstractions in order to extract knowledge that can be exploited "at the point of use" from physicians. The proposed data analysis procedure can be viewed as a Knowledge Discovery in Data Base process that is applied to time-varying data. The work here described is part of a Web-based telemedicine system for the management of Insulin Dependent Diabetes Mellitus patients, called T-IDDM.
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paper_title: Use of data abstraction methods to simplify monitoring.

paper_content:

Abstract I describe the Temporal Control System (TCS), a programming system designed for building intelligent temporal monitoring programs. The ICU data set provided as part of the 1994 AAAI Spring Symposium challenge is used to conduct several experiments. Empirical results from the ICU data set validate the scalable design of the TCS. The remaining experiments examine the computational problem of generating interval values from sample points through persistence assumptions. Using abstractions in combination with persistence assumptions makes the design of higher-level clinical reasoning programs simpler. Abstraction can be used to suppress clinically unimportant details, allowing an expert system to focus on the key information provided by clinical monitors. The TCS provides the framework for the implementation as well as a method of calculating the ‘cost’ of different approaches. To prevent the use of outdated information, it is often useful to limit the time span of a persistent interval. I show that such limitations can be very costly computationally and then show how the application of symbolic abstraction can help. Further performance improvements from switching from continuous to discrete step persistence are shown. These performance enhancing techniques have general applicability.
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paper_title: Temporal Reasoning for Diagnosis in a Causal Probabilistic Knowledge Base

paper_content:

Abstract We have added temporal reasoning to the Heart Disease Program (HDP) to take advantage of the temporal constraints inherent in cardiovascular reasoning. Some processes take place over minutes while others take place over months or years and a strictly probabilistic formalism can generate hypotheses that are impossible given the temporal relationships involved. The HDP has temporal constraints on the causal relations specified in the knowledge base and temporal properties on the patient input provided by the user. These are used in two ways. First, they are used to constrain the generation of the pre-computed causal pathways through the model that speed the generation of hypotheses. Second, they are used to generate time intervals for the instantiated nodes in the hypotheses, which are matched and adjusted as nodes are added to each evolving hypothesis. This domain offers a number of challenges for temporal reasoning. Since the nature of diagnostic reasoning is inferring a causal explanation from the evidence, many of the temporal intervals have few constraints and the reasoning has to make maximum use of those that exist. Thus, the HDP uses a temporal interval representation that includes the earliest and latest beginning and ending specified by the constraints. Some of the disease states can be corrected but some of the manifestations may remain. For example, a valve disease such as aortic stenosis produces hypertrophy that remains long after the valve has been replaced. This requires multiple time intervals to account for the existing findings. This paper discusses the issues and solutions that have been developed for temporal reasoning integrated with a pseudo-Bayesian probabilistic network in this challenging domain for diagnosis.
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paper_title: [Temporal changes and determinants of smoking habits with respect to prevention].

paper_content:

Smoking habits are seen as one of the most important risk factors in developed countries for the incidence of cardiovascular diseases and as the major single cause of premature death. The present study analysed smoking habits in participants of the MONICA-Surveys S1 (1984/85), S2 (1989/90) and S3 (1994/95) concerning prevalence, associated factors, time trends and preventive consequences. An essential aim of these studies was to identify risk groups, that should be addressed in prevention campaigns leading to a reduction of the high mortality due to cardiovascular diseases. The participants of the MONICA-Survey S3 (1994/95) had lower proportions of smokers (31.2 % for men and 22.0 % for women) as compared to estimated proportions of the entire German population (National health survey 1998: 37.4 % und 29.9 %). Women had always a lower proportion of smokers than men in each age group or sociodemographic factor. Strong associations of smoking habits with educational levels, family status and occupational position were observed for men and women. A positive time trend with decreasing smoking prevalence was determined for men. In contrast, an increase in the prevalence of female smokers was observed. The intention to change smoking habits was associated with different factors in men and women. For men, mainly "external" factors like social status or physical activity were important. Women seemed to be more influenced by "internal" matters (somatic complaints, negative self-rated health). Sex-, age- and educational status-specific activities and recommendations are considered to be essential for targeting different person groups in a more focused way.
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paper_title: Extensions to the time-oriented database model to support temporal reasoning in medical expert systems

paper_content:

Extensions to the time-oriented database model to support temporal reasoning in medical expert systems. -
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paper_title: A Causal Bayesian Model for the Diagnosis of Appendicitis

paper_content:

The causal Bayesian approach is based on the assumption that effects (e.g., symptoms) that are not conditionally independent with respect to some causal agent (e.g., a disease) are conditionally independent with respect to some intermediate state caused by the agent, (e.g., a pathological condition). This paper describes the development of a causal Bayesian model for the diagnosis of appendicitis. The paper begins with a description of the standard Bayesian approach to reasoning about uncertainty and the major critiques it faces. The paper then lays the theoretical groundwork for the causal extension of the Bayesian approach, and details specific improvements we have developed. The paper then goes on to describe our knowledge engineering and implementation and the results of a test of the system. The paper concludes with a discussion of how the causal Bayesian approach deals with the criticisms of the standard Bayesian model and why it is superior to alternative approaches to reasoning about uncertainty popular in the AI community.
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paper_title: Verification of temporal scheduling constraints in clinical practice guidelines

paper_content:

The computerization of clinical practice guidelines is a significant scientific challenge for the medical informatics community. One frequently reported factor hindering this objective is the existence of deficiencies within guideline knowledge. In this paper, we focus on the detection of flaws within temporal scheduling constraints. Temporal scheduling constraints are important elements of therapy management, and are frequently incorporated in clinical practice guidelines. We present a suitable verification method that is based on calculating the minimal network of temporal constraints on the execution of guideline activities. Our method serves three purposes: (1) it checks whether temporal scheduling constraints are consistent with scheduling constraints implied by control flow operators and the hierarchical structuring of a guideline; (2) it yields suggestions for an equivalent, yet more explicit representation of non-minimal constraints; (3) it can be used by the guideline interpreter to assemble feasible time intervals for the execution of each guideline activity. We evaluate our approach by applying it to a guideline specified in the Asbru language. For this purpose, we implemented a prototype verifier. Although we concentrate on the guideline representation language Asbru as the demonstration medium of our method within this paper, our approach can be reused to verify several alternative guideline representation formats.
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paper_title: Mining Time-dependent Patient Outcomes from Hospital Patient Records

paper_content:

We describe REMIND, a data mining framework that accurately infers missing clinical information by reasoning over the entire patient record. Hospitals collect computerized patient records (CPR's) in structured (database tables) and unstructured (free text) formats. Structured clinical data in the CPR's is often poorly recorded, and information may be missing about key outcomes and processes. For instance, for a population of 344 colon cancer patients, important clinical outcomes, such as disease state and its evolution, are stored only as unstructured data (doctors' dictations) in the CPR. Raw evidence (extracted directly from the CPR) is not a good predictor of disease state. Yet by combining this evidence in a principled fashion (using methods from uncertain and temporal reasoning), REMIND accurately infers disease state sequences for recurrence, a complex time-varying outcome, for these patients. These outcomes can now be added back into the CPR in structured form.
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paper_title: Contextual and temporal clinical guidelines.

paper_content:

Abstract ::: In this paper, we propose an approach for managing clinical guidelines. We sketch a modular architecture, allowing us to separate conceptually distinct aspects in the management and use of clinical guidelines. In particular, we describe the clinical guidelines knowledge representation module and we sketch the acquisition module. The main focus of the paper is the definition of an expressive formalism for representing clinical guidelines, which allows one to deal with the context dependent character of clinical guidelines and takes into account different temporal aspects.
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paper_title: A prognostic model for temporal courses that combines temporal abstraction and case-based reasoning.

paper_content:

Since clinical management of patients and clinical research are essentially time-oriented endeavours, reasoning about time has become a hot topic in medical informatics. Here we present a method for prognosis of temporal courses, which combines temporal abstractions with case-based reasoning. It is useful for application domains where neither well-known standards, nor known periodicity, nor a complete domain theory exist. We have used our method in two prognostic applications. The first one deals with prognosis of the kidney function for intensive care patients. The idea is to elicit impairments on time, especially to warn against threatening kidney failures. Our second application deals with a completely different domain, namely geographical medicine. Its intention is to compute early warnings against approaching infectious diseases, which are characterised by irregular cyclic occurrences. So far, we have applied our program on influenza and bronchitis. In this paper, we focus on influenza forecast and show first experimental results.
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paper_title: Abstraction on clinical data sequences: an object-oriented data model and a query language based on the event calculus

paper_content:

In this work, we deal with temporal abstraction of clinical data. Abstractions are, for example, blood pressure state (e.g. normal, high, low) and trend (e.g. increasing, decreasing and stationary) over time intervals. The goal of our work is to provide clinicians with automatic tools to extract high-level, concise, important features of available collections of time-stamped clinical data. This capability is especially important when the available collections constantly increase in size, as in long-term clinical follow-up, leading to information overload. The approach we propose exploits the integration of the deductive and object-oriented approaches in clinical databases. The main result of this work is an object-oriented data model based on the event calculus to support temporal abstraction. The proposed approach has been validated building the CARDIOTABS system for the abstraction of clinical data collected during echocardiographic tests.
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paper_title: Temporal consistency checking in clinical guidelines acquisition and execution: the GLARE’s approach

paper_content:

GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent system for the acquisition, representation and execution of clinical guidelines. Temporal constraints play an important role within clinical guidelines (e.g. to specify therapies). The treatment of such constraints is one of the distinguishing features of GLARE. During acquisition, GLARE supports (i) the representation and (ii) the checking of the consistency of the temporal constraints. Moreover, it (iii) automatically checks that the times of execution of specific actions respect the general temporal constraints described in the guideline. This treatment of temporal constraints involves the extension of various Artificial Intelligence (AI) techniques.
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paper_title: Planning and Control

paper_content:

"Planning and Control" explores planning and control by reformulating the two areas in a common control framework, developing the corresponding techniques side-by-side, and identifying opportunities for integrating their ideas and methods. This book is organized around the central roles of prediction, observation, and computation control. The first three chapters deal with predictive models of physical systems based on temporal logic and the differential calculus. Chapter 4 and 5 present some basic concepts in planning and control, including controllability, observability, stability, feedback control, task reduction, conditional plans, and the relationship between goals and preferences. Chapters 6 and 7 consider issues of uncertainty, covering state estimation and the Kalman filter, stochastic dynamic programming, probabilistic modeling, and graph-based decision models. The remaining chapters investigate selected topics in time-critical decision making, adaptive control, and hybrid control architectures. Throughout, the reader is led to consider critical tradeoffs involving the accuracy of prediction, the availability of information from observation, and the costs and benefits of computation in dynamic environments. This book is useful to researchers in artificial intelligence and control theory, and others concerned with the design of complex applications in robotics, automated manufacturing, and time-critical decision support.
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paper_title: A New Approach to Optimal Dynamic Therapy Planning

paper_content:

Therapy planning is a very complex task. One of its crucial aspect is the derivation of therapeutic plans taking into account the dynamic aspect of the decision problem. This paper deals with dynamic decision problems using Influence Views, a novel graphical formalism based on Markov Decision Processes (MDPs) framework, in order to solve decision problems in which the optimal choice has to be revised periodically in accordance to the evolution of the patient's conditions. The proposed methodology has been applied to the plan of the prophylaxis in patients affected by a mild Hereditary Spherocytosis.
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paper_title: Modeling the Temporal Complexities of Symptoms

paper_content:

Objective : To analyze the temporal aspects of symptoms, including their temporal uncertainty, in order to develop a high-level conceptual data model representation of this domain. ::: ::: Design : A basic tenet of existing temporal models is that events occur not only relative to a particular date or time, but also relative to the time of some other event. The time an event occurs, particularly when the event is a symptom being recalled by a patient or collected by a busy provider, is frequently incomplete or uncertain, and this uncertainty must also be represented in a temporal data model. The object-oriented modeling technique used in this study is becoming popular among U.S. medical informatics standards developers. ::: ::: Results : A conceptual data model for the temporal aspects of symptom data, including temporal uncertainty, has been developed. The object-oriented modeling approach used enables the temporal objects and attributes defined in this model to be inherited by other medical objects, such as problems. ::: ::: Conclusion : The temporal comparators presented here have previously been defined, and may serve as the basis for standardizing the terms used to describe how one event temporally relates to another. In an attempt to achieve domain completeness, this study concentrated more on developing a model that is highly expressive than on developing one that is easily queried. This trade-off in representation versus “queryability” will require further analysis and may require modifications to the underlying model.
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paper_title: A framework for knowledge-based temporal abstraction

paper_content:

A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events and contexts. A formal specification of a domain's temporal abstraction knowledge supports acquisition, maintenance, reuse and sharing of that knowledge. ::: ::: The knowledge-based temporal abstraction method decomposes the temporal abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal abstraction mechanisms. The temporal abstraction mechanisms depend on four domain-specific knowledge types: structural, classification (functional), temporal semantic (logical) and temporal dynamic (probabilistic) knowledge. Domain values for all knowledge types are specified when a temporal abstraction system is developed. ::: ::: The knowledge-based temporal abstraction method has been implemented in the RESUME system and has been evaluated in several clinical domains (protocol-based care, monitoring of children's growth and therapy of diabetes) and in an engineering domain (monitoring of traffic control), with encouraging results.
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paper_title: TQuery: a context-sensitive temporal query language.

paper_content:

Users of electronic medical databases request pertinent information by recasting their clinical questions into a formal database query language. Because the query language is the user's only access to the data, the query language must be powerful enough to enable users to express their data requirements. However, a competing need is for the query language to be restrictive enough so that queries can have unambiguous semantics and the query processor can generate correct answers. We describe a query language, called TQuery , that was designed specifically to formulate database queries that are dependent on temporal and contextual relationships. TQuery specifications express contextual constraints without the need to explicitly reference calendar dates. TQuery is the database query language used to retrieve patient data from an object-oriented electronic patient medical-record system called the temporal network (TNET). TNET and TQuery were developed to support the real-time temporal reasoning and representation needs of a LISP workstation-based medical expert system.
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paper_title: A Context-Adaptable Approach to Clinical Guidelines

paper_content:

One of the most relevant obstacles to the use and dissemination of clinical guidelines is the gap between the generality of guidelines (as defined, e.g., by physicians' committees) and the peculiarities of the specific context of application. In particular, general guidelines do not take into account the fact that the tools needed for laboratory and instrumental investigations might be unavailable at a given hospital. Moreover, computer-based guideline managers must also be integrated with the Hospital Information System (HIS), and usually different DBMS are adopted by different hospitals. The GLARE (Guideline Acquisition, Representation and Execution) system addresses these issues by providing a facility for automatic resource-based adaptation of guidelines to the specific context of application, and by providing a modular architecture in which only limited and well-localised changes are needed to integrate the system with the HIS at hand.
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paper_title: Maintaining knowledge about temporal intervals

paper_content:

An interval-based temporal logic is introduced, together with a computationally effective reasoning algorithm based on constraint propagation. This system is notable in offering a delicate balance between
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paper_title: Probabilistic temporal networks: A unified framework for reasoning with time and uncertainty

paper_content:

Complex real-world systems consist of collections of interacting processes/events. These processes change over time in response to both internal and external stimuli as well as to the passage of time itself. Many domains such as real-time systems diagnosis, story understanding, and financial forecasting require the capability to model complex systems under a unified framework to deal with both time and uncertainty. Current models for uncertainty and current models for time already provide rich languages to capture uncertainty and temporal information, respectively. Unfortunately, these semantics have made it extremely difficult to unify time and uncertainty in a way which cleanly and adequately models the problem domains at hand. Existing approaches suffer from significant trade offs between strong semantics for uncertainty and strong semantics for time. In this paper, we explore a new model, the Probabilistic Temporal Network (PTN), for representing temporal and atemporal information while fully embracing probabilistic semantics. The model allows representation of time constrained causality, of when and if events occur, and of the periodic and recurrent nature of processes.
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paper_title: A logic-based calculus of events

paper_content:

An Anthurium plant named 'Krypton' having small, heart shaped purple spathes with a purple spadix, with the spathes being carried above and among the foliage. The leaves are small, dark green and very shiny. The plant has a miniature growth habit, is highly branched, and is suitable for smaller pot sizes (10 cm). The plant grows to a marketable size and begins flowering quickly.
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paper_title: The GLARE approach to clinical guidelines: main features.

paper_content:

In this paper, we present GLARE, a domain-independent prototypical system for acquiring, representing and executing clinical guidelines. GLARE has been built within a 7-year project with Azienda Ospedaliera San Giovanni Battista in Turin (one of the largest hospitals in Italy) and has been successfully tested on clinical guidelines in different domains, including bladder cancer, reflux esophagitis, and heart failure. GLARE is characterized by the adoption of advanced Artificial Intelligence (AI) techniques, to support medical decision making and to manage temporal knowledge.
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paper_title: Natural Language and Text Processing in Biomedicine

paper_content:

After reading this chapter, you should know the answers to these questions: ::: ::: ::: Why is natural language processing (NLP) important? ::: ::: ::: What are the potential uses for NLP in the biomedical domain? ::: ::: ::: What forms of knowledge are used in NLP? ::: ::: ::: What are the principal techniques of NLP? ::: ::: ::: What are the challenges for NLP in the clinical domain? ::: ::: ::: What are the challenges for NLP in the biological domain?
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paper_title: A modular approach for representing and executing clinical guidelines

paper_content:

In this paper, we propose an approach for managing clinical guidelines. We outline a modular architecture, allowing us to separate two conceptually distinct aspects: the representation (and acquisition) of clinical guidelines and their execution. We propose an expressive formalism, which allows one to deal with the context-dependent character of clinical guidelines and also takes into account different temporal aspects. We also describe our tool for acquiring clinical guidelines, which provides a user-friendly interface to physicians, and automatically detects many forms of syntactic and semantic inconsistencies in the guidelines being acquired. In the second part of the paper, we describe a flexible engine for executing clinical guidelines (e.g. for clinical decision support applications, for medical education, or for integrating guidelines into the clinical practice), focusing our attention on temporal issues.
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paper_title: Modeling Medical Reasoning with the Event Calculus: An Application to the Management of Mechanical Ventilation

paper_content:

Explicit representation of time and change is an essential feature for building systems that are supposed to interact with real-world dynamic environments. In this paper, we propose to use the Cached Event Calculus (CEC), an improved version of the Event Calculus [Ko86], to represent temporal aspects in intelligent medical monitoring systems. In particular, we explore the application of CEC to the management of mechanical ventilation, using it to interpret change in data over time, assess patient status and its evolution, and choose the proper level of mechanical assistance.
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paper_title: Towards a comprehensive treatment of temporal constraints in clinical guidelines

paper_content:

In this paper, we focus on an application and extension of artificial intelligence temporal reasoning techniques in order to represent and reason with temporal constraints in clinical guidelines. Particular attention is dedicated to the treatment of repeated (periodic) events, which play a major role in clinical therapies. We also discuss some limitations of our current approach, highlighting possible future enhancements. The work in this paper has been developed in the GLARE project, meant to realize a prototype of a domain-independent manager of clinical guidelines. The GLARE system has been built in cooperation with Azienda Ospedaliera S. Giovanni Battista of Turin, and has been successfully tested on different clinical domains.
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paper_title: Temporal reasoning for decision support in medicine

paper_content:

Objective: Handling time-related concepts is essential in medicine. During diagnosis it can make a substantial difference to know the temporal order in which some symptoms occurred or for how long they lasted. During prognosis the potential evolutions of a disease are conceived as a description of events unfolding in time. In therapy planning the different steps of treatment must be applied in a precise order, with a given frequency and for a certain span of time in order to be effective. This article offers a survey on the use of temporal reasoning for decision support-related tasks in medicine.Material and methods: Key publications of the area, mainly circumscribed to the latest two decades, are reviewed and classified according to three important stages of patient treatment requiring decision support: diagnosis, prognosis and therapy planning/management. Other complementary publications, like those on time-centered information storage and retrieval, are also considered as they provide valuable support to the above mentioned three stages.Results: Key areas are highlighted and used to organize the latest contributions. The survey of previous research is followed by an analysis of what can still be improved and what is needed to make the next generation of decision support systems for medicine more effective.Conclusions: It can be observed that although the area has been considerably developed, there are still areas where more research is needed to make time-based systems of widespread use in decision support-related areas of medicine. Several suggestions for further exploration are proposed as a result of the survey.
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paper_title: A temporal query system for protocol-directed decision support.

paper_content:

Chronus is a query system that supports temporal extensions to the Structured Query Language (SQL) for relational databases. Although the relational data model can store time-stamped data and can permit simple temporal-comparison operations, it does not provide either a closed or a sufficient algebra for manipulating temporal data. In this paper, we outline an algebra that maintains a consistent relational representation of temporal data and that allows the type of temporal queries needed for protocol-directed decision support. We also discuss how Chronus can translate between our temporal algebra and the relational algebra used for SQL queries. We have applied our system to the task of screening patients for clinical trials. Our results demonstrate that Chronous can express sufficiently all required temporal queries, and that the search time of such queries is similar to that of standard SQL.
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paper_title: Toward interactive scheduling systems for managing medical resources

paper_content:

Managers of medico-hospital facilities are facing two general problems when allocating resources to activities: (1) to find an agreement between several and contrasting requirements; (2) to manage dynamic and uncertain situations when constraints suddenly change over time due to medical needs. This paper describes the results of a research aimed at applying constraint-based scheduling techniques to the management of medical resources. A mixed-initiative problem solving approach is adopted in which a user and a decision support system interact to incrementally achieve a satisfactory solution to the problem. A running prototype is described called Interactive Scheduler which offers a set of functionalities for a mixed-initiative interaction to cope with the medical resource management. Interactive Scheduler is endowed with a representation schema used for describing the medical environment, a set of algorithms that address the specific problems of the domain, and an innovative interaction module that offers functionalities for the dialogue between the support system and its user. A particular contribution of this work is the explicit representation of constraint violations, and the definition of scheduling algorithms that aim at minimizing the amount of constraint violations in a solution.
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paper_title: HMAP– A temporal data model managing intervals with different granularities and indeterminacy from natural language sentences

paper_content:

The granularity of given temporal information is the level of abstraction at which information is expressed. Different units of measure allow one to represent different granularities. Indeterminacy is often present in temporal information given at different granularities: temporal indeterminacy is related to incomplete knowledge of when the considered fact happened. Focusing on temporal databases, different granularities and indeterminacy have to be considered in expressing valid time, i.e., the time at which the information is true in the modeled reality. In this paper, we propose HMAP (The term is the transliteration of an ancient Greek poetical word meaning “day”.), a temporal data model extending the capability of defining valid times with different granularity and/or with indeterminacy. In HMAP, absolute intervals are explicitly represented by their start,end, and duration: in this way, we can represent valid times as “in December 1998 for five hours”, “from July 1995, for 15 days”, “from March 1997 to October 15, 1997, between 6 and 6:30 p.m.”. HMAP is based on a three-valued logic, for managing uncertainty in temporal relationships. Formulas involving different temporal relationships between intervals, instants, and durations can be defined, allowing one to query the database with different granularities, not necessarily related to that of data. In this paper, we also discuss the complexity of algorithms, allowing us to evaluate HMAP formulas, and show that the formulas can be expressed as constraint networks falling into the class of simple temporal problems, which can be solved in polynomial time.
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paper_title: Model Formulation: Modeling Electronic Discharge Summaries as a Simple Temporal Constraint Satisfaction Problem

paper_content:

OBJECTIVE ::: To model the temporal information contained in medical narrative reports as a simple temporal constraint satisfaction problem. ::: ::: ::: DESIGN ::: A constraint satisfaction problem is defined by time points and constraints (inequalities between points). A time interval comprises a pair of points and a constraint. Five complete electronic discharge summaries and paragraphs from 226 other discharge summaries were studied. Medical events were represented as intervals, and assertions about events were represented as constraints. Through a consensus process, a set of encoding procedures and a list of issues related to encoding were generated. ::: ::: ::: MEASUREMENTS ::: Instances of temporal disjunction and contradiction and distribution of temporal constraints were used. ::: ::: ::: RESULTS ::: An average of 95 medical events (range, 46-151) and 234 temporal assertions (range, 118-388) were identified per complete discharge summary. Nondefinitional assertions were explicit (36%) or implicit (64%) and absolute (17%), qualitative (72%), or metric (11%). Implicit assertions were based on domain knowledge and assumptions, e.g., the section of the report determined the ordering of events. Issues included linking events, intermittence, periodicity, granularity, vagueness, ambiguity, uncertainty, and plans. ions such as intermittence were not represented explicitly. The temporal network was sparse: Only 0.80% (range, 0.42%-1.38%) of possible constraints were instantiated. No instances of discontinuous temporal disjunction were found in the complete summaries or the 226 paragraphs. One instance of temporal contradiction was found (intrareport rate of 0.2 with a 95% confidence interval of 0.005-1.114). ::: ::: ::: CONCLUSION ::: A simple temporal constraint satisfaction problem appears sufficient to represent most temporal assertions in discharge summaries and may be useful for encoding electronic medical records.
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paper_title: Scenario recognition for temporal reasoning in medical domains

paper_content:

The recognition of high level clinical scenes is fundamental in patient monitoring. In this paper, we propose a technique for recognizing a session, i.e. the clinical process evolution, by comparison against a predetermined set of scenarios, i.e. the possible behaviors for this process. We use temporal constraint networks to represent both scenario and session. Specific operations on networks are then applied to perform the recognition task. An index of temporal proximity is introduced to quantify the degree of matching between two temporal networks in order to select the best scenario fitting a session. We explore the application of our technique, implemented in the Deja Vu system, to the recognition of typical medical scenarios with both precise and imprecise temporal information.
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paper_title: Natural language processing and its future in medicine.

paper_content:

If accurate clinical information were available electronically, automated applications could be developed to use this information to improve patient care and lower costs. However, to be fully retrievable, clinical information must be structured or coded. Many online patient reports are not coded, but are recorded in natural-language text that cannot be reliably accessed. Natural language processing (NLP) can solve this problem by extracting and structuring text-based clinical information, making clinical data available for use. NLP systems are quite difficult to develop, as they require substantial amounts of knowledge, but progress has definitely been made. Some NLP systems have been developed and tested and have demonstrated promising performance in practical clinical applications; some of these systems have already been deployed. The authors provide background information about NLP, briefly describe some of the systems that have been recently developed, and discuss the future of NLP in medicine.
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paper_title: A similarity measure for case based reasoning modeling with temporal abstraction based on crosscorrelation

paper_content:

Adverse drug events (ADEs) are a major limitation of drug safety. They are often caused by inappropriate selection of dose and the concurrent use of drugs modulating each other (drug interaction). Risk assessment and prevention strategies must therefore consider co-administered drugs, individual doses, and their timing. In a new approach we evaluated the performance of cross correlation, commonly used in signal processing, to determine similarities in patient treatments. To achieve this, patient treatments were modeled as groups of vectors representing discrete time intervals. These vectors were cross-correlated and the results evaluated to find clusters in time courses indicating similarity in treatment of different patients. To evaluate our algorithm, we then created a number of test cases. The focus of this article is on each treatment, and its pattern in time and dosage. The algorithm successfully produces a relatively low similarity score for cases that are completely different with respect to their pattern of time and dosage but high scores when they are equal (score of 0.699) or similar (score of 0.528) in their therapies, and thus succeeds in having a relatively high specificity (27/30). Such an approach might help to considerably reduce the problem of false alarms which hampers most existing alerting systems for medication errors or impending ADEs.
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paper_title: Extensions to the time-oriented database model to support temporal reasoning in medical expert systems

paper_content:

Extensions to the time-oriented database model to support temporal reasoning in medical expert systems. -
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paper_title: Verification of temporal scheduling constraints in clinical practice guidelines

paper_content:

The computerization of clinical practice guidelines is a significant scientific challenge for the medical informatics community. One frequently reported factor hindering this objective is the existence of deficiencies within guideline knowledge. In this paper, we focus on the detection of flaws within temporal scheduling constraints. Temporal scheduling constraints are important elements of therapy management, and are frequently incorporated in clinical practice guidelines. We present a suitable verification method that is based on calculating the minimal network of temporal constraints on the execution of guideline activities. Our method serves three purposes: (1) it checks whether temporal scheduling constraints are consistent with scheduling constraints implied by control flow operators and the hierarchical structuring of a guideline; (2) it yields suggestions for an equivalent, yet more explicit representation of non-minimal constraints; (3) it can be used by the guideline interpreter to assemble feasible time intervals for the execution of each guideline activity. We evaluate our approach by applying it to a guideline specified in the Asbru language. For this purpose, we implemented a prototype verifier. Although we concentrate on the guideline representation language Asbru as the demonstration medium of our method within this paper, our approach can be reused to verify several alternative guideline representation formats.
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paper_title: M-HTP: A system for monitoring heart transplant patients

paper_content:

A computer-based assistant for monitoring a patient's clinical course requires the use of tools able to handle temporal issues. Thus, methodologies coming from two historically distinct worlds need to be combined: the traditional world of Data Base Management Systems (DBMS) and the world of Knowledge-Based Systems (KBS). This paper describes an intelligent system designed to assist the clinical staff in the management of a monitoring protocol for infections in heart transplant recipients. The system consists of a DBMS designed for the management of patient clinical data and of a KBS which is capable of reasoning about the large amount of data and embodied in a temporal model based on time-points and intervals. Moreover, the system aims at providing a synthetic view of a patient's clinical history and some diagnostic and therapeutic suggestions. The KBS retrieves findings stored in the data base and creates a complex taxonomy of objects representing a Temporal Network of important events and episodes noted in the patient history; then, from this temporal representation, it develops its reasoning based on medical knowledge represented using frames and production rules. The system is implemented on a Fourth Generation System tool (4GS) and a KBS shell, both running on an IBM PC AT compatible platform.
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paper_title: Temporal Constraint Networks

paper_content:

Abstract This paper extends network-based methods of constraint satisfaction to include continuous variables, thus providing a framework for processing temporal constraints. In this framework, called temporal constraint satisfaction problem (TCSP), variables represent time points and temporal information is represented by a set of unary and binary constraints, each specifying a set of permitted intervals. The unique feature of this framework lies in permitting the processing of metric information, namely, assessments of time differences between events. We present algorithms for performing the following reasoning tasks: finding all feasible times that a given event can occur, finding all possible relationships between two given events, and generating one or more scenarios consistent with the information provided. We distinguish between simple temporal problems (STPs) and general temporal problems, the former admitting at most one interval constraint on any pair of time points. We show that the STP, which subsumes the major part of Vilain and Kautz's point algebra, can be solved in polynomial time. For general TCSPs, we present a decomposition scheme that performs the three reasoning tasks considered, and introduce a variety of techniques for improving its efficiency. We also study the applicability of path consistency algorithms as preprocessing of temporal problems, demonstrate their termination and bound their complexities.
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paper_title: Fuzziness and uncertainty in temporal reasoning

paper_content:

This paper proposes a general discussion of the handling of imprecise and uncertain information in temporal reasoning in the framework of fuzzy sets and possibility theory. The introduction of fuzzy features in temporal reasoning can be related to different issues. First, it can be motivated by the need of a gradual, linguistic-like description of temporal relations even in the face of complete information. An extension of Allen relational calculus is proposed, based on fuzzy comparators expressing linguistic tolerance. Fuzzy Allen relations are defined from a fuzzy partition made by three possible fuzzy relations between dates (approximately equal, clearly smaller, and clearly greater). Second, the handling of fuzzy or incomplete information leads to pervade classical Allen relations, and more generally fuzzy Allen relations, with uncertainty. The paper provides a detailed presentation of the calculus of fuzzy Allen relations (including the composition table of these relations). Moreover, the paper discusses the patterns for propagating uncertainty about (fuzzy) Allen relations in a possibilistic way.
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paper_title: A temporal logic for reasoning about processes and plans

paper_content:

Much previous work in artificial intelligence has neglected representing time in all its complexity. In particular, it has neglected continuous change and the indeterminacy of the future. To rectify this, I have developed a first-order temporal logic, in which it is possible to name and prove things about facts, events, plans, and world histories. In particular, the logic provides analyses of causality, continuous change in quantities, the persistence of facts (the frame problem), and the relationship between tasks and actions. It may be possible to implement a temporal-inference machine based on this logic, which keeps track of several “maps” of a time line, one per possible history.
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paper_title: Verbs and Times

paper_content:

T HE fact that verbs have tenses indicates that considerations involving the concept of time are relevant to their use. These considerations are not limited merely to the obvious discrimination between past, present, and future; there is another, a more subtle dependence on that concept: the use of a verb may also suggest the particular way in which that verb presupposes and involves the notion of time. In a number of recent publications some attention has been paid to these finer aspects, perhaps for the first time systematically. Distinctions have been made among verbs suggesting processes, states, dispositions, occurrences, tasks, achievements, and so on. Obviously these differences cannot be explained in terms of time alone: other factors, like the presence or absence of an object, conditions, intended states of affairs, also enter the picture. Nevertheless one feels that the time element remains crucial; at least it is important enough to warrant separate treatment. Indeed, as I intend to show, if we focus our attention primarily upon the time schemata presupposed by various verbs," we are able to throw light on some of the obscurities which still remain in these matters. These time schemata will appear as important constituents of the concepts that prompt us to use those terms the way we consistently do. There are a few such schemata of very wide application. Once they have been discovered in some typical examples, they may be used as models of comparison in exploring and clarifying the behavior of any verb whatever. In indicating these schemata, I do not claim that they represent all possible ways in which verbs can be used correctly with respect to time determination nor that a verb exhibiting a use fairly covered by one schema cannot have divergent uses, which
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paper_title: Mechanizing temporal knowledge

paper_content:

The importance that an understanding of time plays in many problem-solving situations requires that intelligent programs be equipped with extensive temporal knowledge. This paper discusses one route to that goal, namely the construction of a time specialist, a program knowledgable about time in general which can be used by a higher level program to deal with the temporal aspects of its problem-solving. Some examples are given of such a use of a time specialist. The principal issues addressed in this paper are how the time specialist organizes statements involving temporal references, checks them for consistency, and uses them in answering questions.
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paper_title: Maintaining knowledge about temporal intervals

paper_content:

An interval-based temporal logic is introduced, together with a computationally effective reasoning algorithm based on constraint propagation. This system is notable in offering a delicate balance between
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paper_title: Temporal Ontology And Temporal Reference

paper_content:

A semantics of temporal categories in language and a theory of their use in defining the temporal relations between events both require a more complex structure on the domain underlying the meaning representations than is commonly assumed. This paper proposes an ontology based on such notions as causation and consequence, rather than on purely temporal primitives. A central notion in the ontology is that of an elementary event-complex called a "nucleus." A nucleus can be thought of as an association of a goal event, or "culmination," with a "preparatory process" by which it is accomplished, and a "consequent state," which ensues. Natural-language categories like aspects, futurates, adverbials, and when-clauses are argued to change the temporal/aspectual category of propositions under the control of such a nucleic knowledge representation structure. The same concept of a nucleus plays a central role in a theory of temporal reference, and of the semantics of tense, which we follow McCawley, Partee, and Isard in regarding as an anaphoric category. We claim that any manageable formalism for natural-language temporal descriptions will have to embody such an ontology, as will any usable temporal database for knowledge about events which is to be interrogated using natural language.
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paper_title: Annotating and Reasoning about Time and Events

paper_content:

In this paper we discuss the relationship between TimeML, a rich specification language for event and temporal expressions in text, and the interpretation of these expressions in a temporal semantics. Specifically, we propose to demonstrate how a TimeML markup of text is interpreted within the DAML-Time Ontology and time framework of Hobbs (2002). We demonstrate the expressiveness of TimeML in English text, and discuss the relevance to content specification in the semantic web.
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paper_title: On the problems of time retrieval of temporal relations causality, and coexistence

paper_content:

Intelligent question-answering programs do more than retrieve “raw” data; they make deductive inferences in order to return all valid responses. They report logical inconsistencies, possibly at the data input phase. Similarly, more information is requested from the user if a question asked proves to be ambiguous. A question-answering system of the above type has been designed and implemented. Besides retrieving explicit and implicit temporal relations, the system discovers potentially causal relationships which also satisfy different time restrictions. Questions concerning a generalized concept of coexistence can also be answered. It is hoped that programs of a similar nature will become of much pragmatic use to researchers in physics, chemistry, biology, and so on, in evaluating complex, interrelated experimental data. Several additional applications for this type of program are mentioned, ranging from problems in criminology to air traffic control. The Associative Memory, Parallel Processing Language, AMPPL-II, was found rather satisfactory for the project. It is finally suggested that the system being described could serve as a component in a complex cognitive mechanism.
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paper_title: The Logical Approach to Temporal Reasoning

paper_content:

Temporal reasoning started to be considered as a subject of study in artificial intelligence in the late 1970's. Since then several ways to represent and use temporal knowledge have been suggested. As a result of that, there are several formalisms capable of coping with temporal notions in some way or other. They range from isolated proposals to complex systems where the temporal aspect is used together with other important features for the task of modelling an intelligent agent. The purposes of this article are to summarize logic-based temporal reasoning research and give a glance on the different research tracks envisaging future lines of research. It is intended to be useful to those who need to be involved in systems having these characteristics and also an occasion to present newcomers some problems in the area that still waits for a solution.
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paper_title: 1 Temporally Anchoring and Ordering Events in News

paper_content:

This paper describes a domain-independent approach to temporally anchoring and ordering events in news. The focus is on event-event and event-time linking. The approach involves mixed-initiative corpus annotation, with automatic preprocessing to identify clause structure, tense, aspect, and temporal adverbials. A controlled experiment reveals the capabilities of humans in ordering events in news. The paper then develops and evaluates two different approaches to machine learning of ordering information: learning from event-event ordering judgments, and learning from event-time anchoring judgments.
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paper_title: Temporal logic : mathematical foundations and computational aspects

paper_content:

1. The handling of time: introduction and survey 2. Semantical presentation of temporal connectives 3. Axiomatic presentation of propositional temporal connectives 4. Predicate temporal logics 5. Temporal logics presented in the predicate calculus: language-metalanguage 6. A general theory of axiomatization for propositional temporal logic 7. Basic many-dimensional systems 8. Propositional quantifiers and fixed point operators 9. Expressive power of one-dimensional temporal connectives: basic concepts 10. Expressive completeness of Since and Until over integer and real time 11. Expressive completeness of Stavi connectives over general linear time 12. Further expressive completeness results 13. H-dimension 14. Adding a temporal dimension to a logic system 15. Decidability in temporal logic Bibliography Notation Index Index of Systems Index
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paper_title: Tense As Discourse Anaphor

paper_content:

In this paper, I consider a range of English expressions and show that their context-dependency can be characterized in terms of two properties:1. They specify entities in an evolving model of the discourse that the listener is constructing;2. The particular entity specified depends on another entity in that part of the evolving "discourse model" that the listener is currently attending to.Such expressions have been called anaphors. I show how tensed clauses share these characteristics, usually just attributed to anaphoric noun phrases. This not only allows us to capture in a simple way the oft-stated but difficult-to-prove intuition that tense is anaphoric, but also contributes to our knowledge of what is needed for understanding narrative text.
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paper_title: Aspect, Aspectual Class, And The Temporal Structure Of Narrative

paper_content:

This paper consists of two parts. The first part discusses commonsense knowledge about events as manifested in language. Three kinds of knowledge are identified: compositional, durational, and aspectual. Compositional knowledge concerns internal structuring of events into preparatory, initial, main (the body), final, and resulting stages. Durational knowledge concerns durational relations between events and stages of the same event. Durational knowledge can be expressed as qualitative dependencies among the parameters of the event and as its time scale. The notion of time scale is introduced and related to shared cyclical events (time units).In discussing aspectual knowledge three notions are distinguished: aspect as a grammatical category of the verb, implemented by affixes, auxiliaries, and such; aspectual class, which is a characteristic of a lexical meaning; and the aspectual perspective of the sentence determined by the position of the Reference Time (RT) with respect to the event described by a finite clause. I argue that an aspectual classification of situations evolving in time should be based on such considerations as the kinds of resources they consume and the goals they achieve. A detailed classification of instantaneous and noninstantaneous events is developed.The second part of the paper discusses how this knowledge is employed in understanding extended narratives. Temporal discontinuities, in conjunction with other kinds of discontinuities identified in the paper, signal boundaries between discourse segments; within each segment, all three varieties of temporal knowledge help establish the temporal relations among narrated events.
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paper_title: A Computational Model Of The Semantics Of Tense And Aspect

paper_content:

The PUNDIT natural-language system processes references to situations and the intervals over which they hold using an algorithm that integrates the analysis of tense and aspect. For each tensed clause, PUNDIT processes the main verb and its grammatical categories of tense, perfect, and progressive in order to extract three complementary pieces of temporal information. The first is whether a situation has actual time associated with it. Secondly, for each situation that is presumed to take place in actual time, PUNDIT represents its temporal structure as one of three situation types: a state, process, or transition event. The temporal structures of each of these situation types consist of one or more intervals. The intervals are characterized by two features: kinesis, which pertains to their internal structure, and boundedness, which constrains the manner in which they get located in time. Thirdly, the computation of temporal location exploits the three temporal indices proposed in Reichenbach 1947: event time, speech time, and reference time. Here, however, event time is formulated as a single component of the full temporal structure of a situation in order to provide an integrated treatment of tense and aspect.
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paper_title: Temporal information in newswire articles : an annotation scheme and corpus study

paper_content:

Many natural language processing applications, such as information extraction, question ::: answering, topic detection and tracking, would benefit significantly from the ability to ::: accurately position reported events in time, either relatively with respect to other events or ::: absolutely with respect to calendrical time. However, relatively little work has been done ::: to date on the automatic extraction of temporal information from text. ::: Before we can progress to automatically position reported events in time, we must gain ::: an understanding of the mechanisms used to do this in language. This understanding can ::: be promoted through the development of all annotation scheme, which allows us to identify ::: the textual expressions conveying events, times and temporal relations in a corpus of 'real' ::: text. ::: This thesis describes a fine-grained annotation scheme with which we can capture all ::: events, times and temporal relations reported ill a text. To aid the application of the scheme ::: to text, a graphical annotation tool has been developed. This tool not only allows easy markup ::: of sophisticated temporal annotations, it also contains an interactive, inference-based ::: component supporting the gathering of temporal relations. The annotation scheme and the ::: tool have been evaluated through the construction of a trial corpus during a pilot study. In ::: this study, a group of annotators was supplied with a description of the annotation scheme ::: and asked to apply it to a trial corpus. ::: The pilot study showed that the annotation scheme was difficult to apply, but is feasible ::: with improvements to the definition of the annotation scheme and the tool. Analysis of ::: the resulting trial corpus also provides preliminary results on the relative extent to which ::: different linguistic mechanisms, explicit and implicit, are used to convey temporal relational ::: information in text.
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paper_title: Medical language processing: applications to patient data representation and automatic encoding.

paper_content:

A linguistic approach is presented to develop a representation of patient data. Semantic categories developed for computer processing of narrative clinical reports are shown to be similar to the Medical Concepts used manually to extract data from narrative in Exercises of the Computer-based Patient Record Institute. Clinical statement types composed of these categories are used in the Linguistic String Project (LSP) medical language processing (MLP) system to convert narrative information into relational database tables of patient information. A procedure for mapping the output of the LSP MLP system into SNOMED International codes was developed. Preliminary results and further requirements are discussed.
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paper_title: Automated Encoding of Clinical Documents Based on Natural Language Processing

paper_content:

Objective: The aim of this study was to develop a method based on natural language processing (NLP) that automatically maps an entire clinical document to codes with modifiers and to quantitatively evaluate the method. ::: ::: Methods: An existing NLP system, MedLEE, was adapted to automatically generate codes. The method involves matching of structured output generated by MedLEE consisting of findings and modifiers to obtain the most specific code. Recall and precision applied to Unified Medical Language System (UMLS) coding were evaluated in two separate studies. Recall was measured using a test set of 150 randomly selected sentences, which were processed using MedLEE. Results were compared with a reference standard determined manually by seven experts. Precision was measured using a second test set of 150 randomly selected sentences from which UMLS codes were automatically generated by the method and then validated by experts. ::: ::: Results: Recall of the system for UMLS coding of all terms was .77 (95% CI .72–.81), and for coding terms that had corresponding UMLS codes recall was .83 (.79–.87). Recall of the system for extracting all terms was .84 (.81–.88). Recall of the experts ranged from .69 to .91 for extracting terms. The precision of the system was .89 (.87–.91), and precision of the experts ranged from .61 to .91. ::: ::: Conclusion: Extraction of relevant clinical information and UMLS coding were accomplished using a method based on NLP. The method appeared to be comparable to or better than six experts. The advantage of the method is that it maps text to codes along with other related information, rendering the coded output suitable for effective retrieval.
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paper_title: Computerized extraction of coded findings from free-text radiologic reports. Work in progress.

paper_content:

A computerized data acquisition tool, the special purpose radiology understanding system (SPRUS), has been implemented as a module in the Health Evaluation through Logical Processing Hospital Information System. This tool uses semantic information from a diagnostic expert system to parse free-text radiology reports and to extract and encode both the findings and the radiologists' interpretations. These coded findings and interpretations are then stored in a clinical data base. The system recognizes both radiologic findings and diagnostic interpretations. Initial tests showed a true-positive rate of 87% for radiographic findings and a bad data rate of 5%. Diagnostic interpretations are recognized at a rate of 95% with a bad data rate of 6%. Testing suggests that these rates can be improved through enhancements to the system's thesaurus and the computerized medical knowledge that drives it. This system holds promise as a tool to obtain coded radiologic data for research, medical audit, and patient care.
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paper_title: Natural language processing in medicine: an overview.

paper_content:

An overview is given of natural language processing applications in medicine. An attempt has been made to enumerate the most important and known international projects and to summarize their goals, principles, methods and results. A section is devoted to projects involving the Dutch language. A more general discussion about the two fundamental approaches concerning medical language understanding is provided. An extensive bibliography may be useful for those wishing to explore this research domain.
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paper_title: Medical Language Processing Computer Management Of Narrative Data

paper_content:

Thank you very much for reading medical language processing computer management of narrative data. Maybe you have knowledge that, people have search numerous times for their favorite novels like this medical language processing computer management of narrative data, but end up in malicious downloads. Rather than enjoying a good book with a cup of coffee in the afternoon, instead they are facing with some malicious bugs inside their desktop computer.
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paper_title: 1 Temporally Anchoring and Ordering Events in News

paper_content:

This paper describes a domain-independent approach to temporally anchoring and ordering events in news. The focus is on event-event and event-time linking. The approach involves mixed-initiative corpus annotation, with automatic preprocessing to identify clause structure, tense, aspect, and temporal adverbials. A controlled experiment reveals the capabilities of humans in ordering events in news. The paper then develops and evaluates two different approaches to machine learning of ordering information: learning from event-event ordering judgments, and learning from event-time anchoring judgments.
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paper_title: Natural language processing and its future in medicine.

paper_content:

If accurate clinical information were available electronically, automated applications could be developed to use this information to improve patient care and lower costs. However, to be fully retrievable, clinical information must be structured or coded. Many online patient reports are not coded, but are recorded in natural-language text that cannot be reliably accessed. Natural language processing (NLP) can solve this problem by extracting and structuring text-based clinical information, making clinical data available for use. NLP systems are quite difficult to develop, as they require substantial amounts of knowledge, but progress has definitely been made. Some NLP systems have been developed and tested and have demonstrated promising performance in practical clinical applications; some of these systems have already been deployed. The authors provide background information about NLP, briefly describe some of the systems that have been recently developed, and discuss the future of NLP in medicine.
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paper_title: Retrieving time information from natural-language texts

paper_content:

An understanding of time relations is central to processing information contained in a narrative. A typical narrative is concerned with the relative ordering or progression of events over time, and the information that one would like to retrieve is often of the type 'What happened after event x?' or 'Did event x precede event y?' Determination of causality also requires a knowledge of time relations, since an event x can cause an event y only if it precedes event y in time. There has been considerable interest in the processing of time information among researchers in artificial intelligence. Several systems have been designed which compute time relations from a structured input of time specifications. Kahn and Gorry (1977) present a 'time specialist' program which accepts input about time relations in a LISP-like form; from this input, the 'specialist' comp~tes time relations, checks for inconsistencies in the input and answers questic~ns about time relations between the events given in the input. The speciali~' provides several different ways of organising the time
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paper_title: Mining Time-dependent Patient Outcomes from Hospital Patient Records

paper_content:

We describe REMIND, a data mining framework that accurately infers missing clinical information by reasoning over the entire patient record. Hospitals collect computerized patient records (CPR's) in structured (database tables) and unstructured (free text) formats. Structured clinical data in the CPR's is often poorly recorded, and information may be missing about key outcomes and processes. For instance, for a population of 344 colon cancer patients, important clinical outcomes, such as disease state and its evolution, are stored only as unstructured data (doctors' dictations) in the CPR. Raw evidence (extracted directly from the CPR) is not a good predictor of disease state. Yet by combining this evidence in a principled fashion (using methods from uncertain and temporal reasoning), REMIND accurately infers disease state sequences for recurrence, a complex time-varying outcome, for these patients. These outcomes can now be added back into the CPR in structured form.
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paper_title: Development and evaluation of a computerized admission diagnoses encoding system.

paper_content:

Hospital information systems designed to support the needs of health care professionals include patient data entered using both freetext and precoded storage schemes. A major disadvantage of freetext storage schemes is that data captured in this format can only be presented as is to the user for review tasks. In the view of many health care scientists, natural language understanding systems capable of identifying, extracting, and encoding information contained in freetext data may provide the necessary tools to overcome this weakness. This paper describes the development and evaluation of a such a system designed to encode freetext admission diagnoses. This system combines both semantic and syntactic linguistic analysis techniques. Evaluation results demonstrate the overall performance of this system to be reasonable, accurately encoding approximately 76% of admission diagnoses. Inefficiencies are primarily due to the inability of this system to generate encodings in roughly 15% of test cases. When encodings are produced, however, accuracy equals that of the current manual coding method. With further modification, this application can partially automate the coding process.
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paper_title: Automation of a problem list using natural language processing

paper_content:

BackgroundThe medical problem list is an important part of the electronic medical record in development in our institution. To serve the functions it is designed for, the problem list has to be as accurate and timely as possible. However, the current problem list is usually incomplete and inaccurate, and is often totally unused. To alleviate this issue, we are building an environment where the problem list can be easily and effectively maintained.MethodsFor this project, 80 medical problems were selected for their frequency of use in our future clinical field of evaluation (cardiovascular). We have developed an Automated Problem List system composed of two main components: a background and a foreground application. The background application uses Natural Language Processing (NLP) to harvest potential problem list entries from the list of 80 targeted problems detected in the multiple free-text electronic documents available in our electronic medical record. These proposed medical problems drive the foreground application designed for management of the problem list. Within this application, the extracted problems are proposed to the physicians for addition to the official problem list.ResultsThe set of 80 targeted medical problems selected for this project covered about 5% of all possible diagnoses coded in ICD-9-CM in our study population (cardiovascular adult inpatients), but about 64% of all instances of these coded diagnoses. The system contains algorithms to detect first document sections, then sentences within these sections, and finally potential problems within the sentences. The initial evaluation of the section and sentence detection algorithms demonstrated a sensitivity and positive predictive value of 100% when detecting sections, and a sensitivity of 89% and a positive predictive value of 94% when detecting sentences.ConclusionThe global aim of our project is to automate the process of creating and maintaining a problem list for hospitalized patients and thereby help to guarantee the timeliness, accuracy and completeness of this information.
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paper_title: A General Natural-language Text Processor for Clinical Radiology

paper_content:

Objective : Development of a general natural-language processor that identifies clinical information in narrative reports and maps that information into a structured representation containing clinical terms. ::: ::: Design : The natural-language processor provides three phases of processing, all of which are driven by different knowledge sources. The first phase performs the parsing. It identifies the structure of the text through use of a grammar that defines semantic patterns and a target form. The second phase, regularization, standardizes the terms in the initial target structure via a compositional mapping of multi-word phrases. The third phase, encoding, maps the terms to a controlled vocabulary. Radiology is the test domain for the processor and the target structure is a formal model for representing clinical information in that domain. ::: ::: Measurements : The impression sections of 230 radiology reports were encoded by the processor. Results of an automated query of the resultant database for the occurrences of four diseases were compared with the analysis of a panel of three physicians to determine recall and precision. ::: ::: Results : Without training specific to the four diseases, recall and precision of the system(combined effect of the processor and query generator) were 70% and 87%. Training of the query component increased recall to 85% without changing precision.
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paper_title: Evaluating the UMLS as a source of lexical knowledge for medical language processing.

paper_content:

Abstract ::: Medical language processing (MLP) systems rely on specialized lexicons in order to recognize, classify, and normalize medical terminology, and the performance of an MLP system is dependent on the coverage and quality of such lexicons. However, the acquisition of lexical knowledge is expensive and time-consuming. The UMLS is a comprehensive resource that can be used to acquire lexical knowledge needed for medical language processing. This paper describes methods that use these resources to automatically create lexical entries and generate two lexicons. The first lexicon was created primarily using the UMLS, whereas the second was created by supplementing the lexicon of an existing MLP system called MedLEE with entries based on the UMLS. We subsequently carried out a study, which is the primary focus of this paper, using MedLEE with each of the two lexicons and also the current MedLEE lexicon to measure performance. Overall accuracy, sensitivity, and specificity using the lexicon primarily based on the UMLS were.86,.60, and.96 respectively. Those measures using the MedLEE lexicon alone were.93,.81, and.93, which was significantly better except for specificity; performance using the supplemental lexicon was exactly the same as performance using solely the MedLEE lexicon.
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paper_title: Automatic Detection of Acute Bacterial Pneumonia from Chest X-ray Reports

paper_content:

OBJECTIVE ::: To evaluate the performance of a natural language processing system in extracting pneumonia-related concepts from chest x-ray reports. ::: ::: ::: METHODS ::: ::: ::: ::: DESIGN ::: Four physicians, three lay persons, a natural language processing system, and two keyword searches (designated AAKS and KS) detected the presence or absence of three pneumonia-related concepts and inferred the presence or absence of acute bacterial pneumonia from 292 chest x-ray reports. Gold standard: Majority vote of three independent physicians. Reliability of the gold standard was measured. ::: ::: ::: OUTCOME MEASURES ::: Recall, precision, specificity, and agreement (using Finn's R: statistic) with respect to the gold standard. Differences between the physicians and the other subjects were tested using the McNemar test for each pneumonia concept and for the disease inference of acute bacterial pneumonia. ::: ::: ::: RESULTS ::: Reliability of the reference standard ranged from 0.86 to 0.96. Recall, precision, specificity, and agreement (Finn R:) for the inference on acute bacterial pneumonia were, respectively, 0.94, 0.87, 0.91, and 0.84 for physicians; 0.95, 0.78, 0.85, and 0.75 for natural language processing system; 0.46, 0.89, 0.95, and 0.54 for lay persons; 0.79, 0.63, 0.71, and 0.49 for AAKS; and 0.87, 0.70, 0.77, and 0.62 for KS. The McNemar pairwise comparisons showed differences between one physician and the natural language processing system for the infiltrate concept and between another physician and the natural language processing system for the inference on acute bacterial pneumonia. The comparisons also showed that most physicians were significantly different from the other subjects in all pneumonia concepts and the disease inference. ::: ::: ::: CONCLUSION ::: In extracting pneumonia related concepts from chest x-ray reports, the performance of the natural language processing system was similar to that of physicians and better than that of lay persons and keyword searches. The encoded pneumonia information has the potential to support several pneumonia-related applications used in our institution. The applications include a decision support system called the antibiotic assistant, a computerized clinical protocol for pneumonia, and a quality assurance application in the radiology department.
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paper_title: MPLUS: A Probabilistic Medical Language Understanding System

paper_content:

This paper describes the basic philosophy and implementation of MPLUS (M+), a robust medical text analysis tool that uses a semantic model based on Bayesian Networks (BNs). BNs provide a concise and useful formalism for representing semantic patterns in medical text, and for recognizing and reasoning over those patterns. BNs are noise-tolerant, and facilitate the training of M+.
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paper_title: Computerized extraction of coded findings from free-text radiologic reports. Work in progress.

paper_content:

A computerized data acquisition tool, the special purpose radiology understanding system (SPRUS), has been implemented as a module in the Health Evaluation through Logical Processing Hospital Information System. This tool uses semantic information from a diagnostic expert system to parse free-text radiology reports and to extract and encode both the findings and the radiologists' interpretations. These coded findings and interpretations are then stored in a clinical data base. The system recognizes both radiologic findings and diagnostic interpretations. Initial tests showed a true-positive rate of 87% for radiographic findings and a bad data rate of 5%. Diagnostic interpretations are recognized at a rate of 95% with a bad data rate of 6%. Testing suggests that these rates can be improved through enhancements to the system's thesaurus and the computerized medical knowledge that drives it. This system holds promise as a tool to obtain coded radiologic data for research, medical audit, and patient care.
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paper_title: A broad-coverage natural language processing system

paper_content:

Natural language processing systems (NLP) that extract clinical information from textual reports were shown to be effective for limited domains and for particular applications. Because an NLP system typically requires substantial resources to develop, it is beneficial if it is designed to be easily extendible to multiple domains and applications. This paper describes multiple extensions of an NLP system called MedLEE, which was originally developed for the domain of radiological reports of the chest, but has subsequently been extended to mammography, discharge summaries, all of radiology, electrocardiography, echocardiography, and pathology.
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paper_title: Experience with a mixed semantic/syntactic parser.

paper_content:

The value of the computerized medical record is derived in part from the availability of medical information in a coded form accessible to manipulation by processes designed for automated decision support, medical research, and computer assistance in the management of health care delivery. To meet these needs medical reports captured and stored as natural language documents must be encoded. Below we discuss an ongoing formative process aimed at developing a natural language understanding system for chest x-ray reports. Comparative data showing the progress of this process is presented.
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paper_title: Automating SNOMED coding using medical language understanding: a feasibility study.

paper_content:

This paper evaluates qualitatively the use of the MedLEE natural language processing system to code medical narratives directly into the SNOMED nomenclature, while retaining the MedLEE information model data structure. A gold standard is produced from narrative text manually coded in SNOMED. An automated parsing and SNOMED-coding of the narrative text is then automatically generated by MedLEE. By comparing MedLEE s output to that of the Gold Standard, the capacities of SNOMED and MedLEE to represent the clinical information are subsequently evaluated leading to qualitative observations on their respective strengths and constraints. In this study, MedLEE did code to SNOMED and captures the codes in a sub-structure amenable to interoperability with the description logic of SNOMED RT, showing an approach that augments and formalizes SNOMED s compositional representation methods to accurately capture information from clinical narratives.
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paper_title: Discourse structures in medical reports—Watch out! The generation of referentially coherent and valid text knowledge bases in the medSYNDIKATE system

paper_content:

The automatic analysis of medical narratives currently suffers from neglecting text structure phenomena such as referential relations between discourse units. This has unwarranted effects on the descriptional adequacy of medical knowledge bases automatically generated from texts. The resulting representation bias can be characterized in terms of incomplete, artificially fragmented and referentially invalid knowledge structures. We focus here on four basic types of textual reference relations,6iz. pronominal and nominal anaphora, textual ellipsis and metonymy and show how to deal with them in an adequate text parsing device. Since the types of reference relations we discuss show an increasing dependence on conceptual background knowledge, we stress the need for formally grounded, expressive conceptual representation systems for medical knowledge. Our suggestions are based on experience with MEDSYNDIKATE, a medical text knowledge acquisition system designed to properly deal with various sorts of discourse structure phenomena. © 1999 Elsevier Science Ireland Ltd. All rights reserved.
---
paper_title: System Architecture for Temporal Information Extraction, Representationand Reasoning in Clinical Narrative Reports

paper_content:

Exploring temporal information in narrative Electronic Medical Records (EMRs) is essential and challenging. We propose an architecture for an integrated approach to process temporal information in clinical narrative reports. The goal is to initiate and build a foundation that supports applications which assist healthcare practice and research by including the ability to determine the time of clinical events (e.g., past vs. present). Key components include: (1) a temporal constraint structure for temporal expressions and the development of an associated tagger; (2) a Natural Language Processing (NLP) system for encoding and extracting medical events and associating them with formalized temporal data; (3) a post-processor, with a knowledge-based subsystem to help discover implicit information, that resolves temporal expressions and deals with issues such as granularity and vagueness; and (4) a reasoning mechanism which models clinical reports as Simple Temporal Problems (STPs).
---
paper_title: A temporal constraint structure for extracting temporal information from clinical narrative

paper_content:

Introduction. Time is an essential element in medical data and knowledge which is intrinsically connected with medical reasoning tasks. Many temporal reasoning mechanisms use constraint-based approaches. Our previous research demonstrates that electronic discharge summaries can be modeled as a simple temporal problem (STP).Objective. To categorize temporal expressions in clinical narrative text and to propose and evaluate a temporal constraint structure designed to model this temporal information and to support the implementation of higher-level temporal reasoning.Methods. A corpus of 200 random discharge summaries across 18 years was applied in a grounded approach to construct a representation structure. Then, a subset of 100 discharge summaries was used to tally the frequency of each identified time category and the percentage of temporal expressions modeled by the structure. Fifty random expressions were used to assess inter-coder agreement.Results. Six main categories of temporal expressions were identified. The constructed temporal constraint structure models time over which an event occurs by constraining its starting time and ending time. It includes a set of fields for the endpoint(s) of an event, anchor information, qualitative and metric temporal relations, and vagueness. In 100 discharge summaries, 1961 of 2022 (97%) identified temporal expressions were effectively modeled using the temporal constraint structure. Inter-coder evaluation of 50 expressions yielded exact match in 90%, partial match with trivial differences in 8%, partial match with large differences in 2%, and total mismatch in 0%.Conclusion. The proposed temporal constraint structure embodies a sufficient and successful implementation method to encode the diversity of temporal information in discharge summaries. Placing data within the structure provides a foundational representation upon which further reasoning, including the addition of domain knowledge and other post-processing to implement an STP, can be accomplished.
---
paper_title: Model Formulation: Modeling Electronic Discharge Summaries as a Simple Temporal Constraint Satisfaction Problem

paper_content:

OBJECTIVE ::: To model the temporal information contained in medical narrative reports as a simple temporal constraint satisfaction problem. ::: ::: ::: DESIGN ::: A constraint satisfaction problem is defined by time points and constraints (inequalities between points). A time interval comprises a pair of points and a constraint. Five complete electronic discharge summaries and paragraphs from 226 other discharge summaries were studied. Medical events were represented as intervals, and assertions about events were represented as constraints. Through a consensus process, a set of encoding procedures and a list of issues related to encoding were generated. ::: ::: ::: MEASUREMENTS ::: Instances of temporal disjunction and contradiction and distribution of temporal constraints were used. ::: ::: ::: RESULTS ::: An average of 95 medical events (range, 46-151) and 234 temporal assertions (range, 118-388) were identified per complete discharge summary. Nondefinitional assertions were explicit (36%) or implicit (64%) and absolute (17%), qualitative (72%), or metric (11%). Implicit assertions were based on domain knowledge and assumptions, e.g., the section of the report determined the ordering of events. Issues included linking events, intermittence, periodicity, granularity, vagueness, ambiguity, uncertainty, and plans. ions such as intermittence were not represented explicitly. The temporal network was sparse: Only 0.80% (range, 0.42%-1.38%) of possible constraints were instantiated. No instances of discontinuous temporal disjunction were found in the complete summaries or the 226 paragraphs. One instance of temporal contradiction was found (intrareport rate of 0.2 with a 95% confidence interval of 0.005-1.114). ::: ::: ::: CONCLUSION ::: A simple temporal constraint satisfaction problem appears sufficient to represent most temporal assertions in discharge summaries and may be useful for encoding electronic medical records.
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paper_title: medSynDiKATe—a natural language system for the extraction of medical information from findings reports

paper_content:

Abstract medSyn D iKATe is a natural language processor, which automatically acquires medical information from findings reports. In the course of text analysis their contents is transferred to conceptual representation structures, which constitute a corresponding text knowledge base. medSyn D iKATe is particularly adapted to deal properly with text structures, such as various forms of anaphoric reference relations spanning several sentences. The strong demands medSyn D iKATe poses on the availability of expressive knowledge sources are accounted for by two alternative approaches to acquire medical domain knowledge (semi)automatically. We also present data for the information extraction performance of medSyn D iKATe in terms of the semantic interpretation of three major syntactic patterns in medical documents.
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paper_title: Temporal Constraint Networks

paper_content:

Abstract This paper extends network-based methods of constraint satisfaction to include continuous variables, thus providing a framework for processing temporal constraints. In this framework, called temporal constraint satisfaction problem (TCSP), variables represent time points and temporal information is represented by a set of unary and binary constraints, each specifying a set of permitted intervals. The unique feature of this framework lies in permitting the processing of metric information, namely, assessments of time differences between events. We present algorithms for performing the following reasoning tasks: finding all feasible times that a given event can occur, finding all possible relationships between two given events, and generating one or more scenarios consistent with the information provided. We distinguish between simple temporal problems (STPs) and general temporal problems, the former admitting at most one interval constraint on any pair of time points. We show that the STP, which subsumes the major part of Vilain and Kautz's point algebra, can be solved in polynomial time. For general TCSPs, we present a decomposition scheme that performs the three reasoning tasks considered, and introduce a variety of techniques for improving its efficiency. We also study the applicability of path consistency algorithms as preprocessing of temporal problems, demonstrate their termination and bound their complexities.
---
paper_title: Extracting numeric measurements and temporal coordinates from Japanese radiological reports

paper_content:

Medical records are written mainly, in natural language. The focus of this study is narrative radiological reports written in natural Japanese. These reports cannot be used for advanced retrieval, data mining, and so on, unless they are stored, using a structured format such as DICOM-SR. The goal is to structure narrative reports progressively, using natural language processing (NLP). Structure has many different levels, for example, DICOM-SR has three established levels -- basic text , enhanced and comprehensive . At the enhanced level, it is necessary to use numerical measurements and spatial & temporal coordinates . In this study, the wording used in the reports was first standardized, dictionaries were organized, and morphological analysis performed. Next, numerical measurements and temporal coordinates were extracted, and the objects to which they referred, analyzed. 10,000 CT and MR reports were separated into 82,122 sentences, and 34,269 of the 36,444 numerical descriptions were tagged. Periods, slashes, hyphens, and parentheses are ambiguously used in the description of enumerated lists, dates, image numbers, and anatomical names, as well as at the end of sentences; to resolve this ambiguity, descriptions were processed, according to the order -- date, size, unit, enumerated list, and abbreviation -- then, the tagged reports were separated into sentences.
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paper_title: Modeling the Temporal Complexities of Symptoms

paper_content:

Objective : To analyze the temporal aspects of symptoms, including their temporal uncertainty, in order to develop a high-level conceptual data model representation of this domain. ::: ::: Design : A basic tenet of existing temporal models is that events occur not only relative to a particular date or time, but also relative to the time of some other event. The time an event occurs, particularly when the event is a symptom being recalled by a patient or collected by a busy provider, is frequently incomplete or uncertain, and this uncertainty must also be represented in a temporal data model. The object-oriented modeling technique used in this study is becoming popular among U.S. medical informatics standards developers. ::: ::: Results : A conceptual data model for the temporal aspects of symptom data, including temporal uncertainty, has been developed. The object-oriented modeling approach used enables the temporal objects and attributes defined in this model to be inherited by other medical objects, such as problems. ::: ::: Conclusion : The temporal comparators presented here have previously been defined, and may serve as the basis for standardizing the terms used to describe how one event temporally relates to another. In an attempt to achieve domain completeness, this study concentrated more on developing a model that is highly expressive than on developing one that is easily queried. This trade-off in representation versus “queryability” will require further analysis and may require modifications to the underlying model.
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paper_title: HMAP– A temporal data model managing intervals with different granularities and indeterminacy from natural language sentences

paper_content:

The granularity of given temporal information is the level of abstraction at which information is expressed. Different units of measure allow one to represent different granularities. Indeterminacy is often present in temporal information given at different granularities: temporal indeterminacy is related to incomplete knowledge of when the considered fact happened. Focusing on temporal databases, different granularities and indeterminacy have to be considered in expressing valid time, i.e., the time at which the information is true in the modeled reality. In this paper, we propose HMAP (The term is the transliteration of an ancient Greek poetical word meaning “day”.), a temporal data model extending the capability of defining valid times with different granularity and/or with indeterminacy. In HMAP, absolute intervals are explicitly represented by their start,end, and duration: in this way, we can represent valid times as “in December 1998 for five hours”, “from July 1995, for 15 days”, “from March 1997 to October 15, 1997, between 6 and 6:30 p.m.”. HMAP is based on a three-valued logic, for managing uncertainty in temporal relationships. Formulas involving different temporal relationships between intervals, instants, and durations can be defined, allowing one to query the database with different granularities, not necessarily related to that of data. In this paper, we also discuss the complexity of algorithms, allowing us to evaluate HMAP formulas, and show that the formulas can be expressed as constraint networks falling into the class of simple temporal problems, which can be solved in polynomial time.
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paper_title: Temporal Granularity: Completing the Puzzle

paper_content:

Granularity is an integral feature of both anchored (e.g., 25 October 1995, July 1996) and unanchored (e.g., 3 minutes, 6 hours 20 minutes, 5 days) temporal data. In supporting temporal data that is specified in different granularities, numerous approaches have been proposed to deal with the issues of converting temporal data from one granularity to another. The emphasis, however, has only been on granularity conversions with respect to anchored temporal data. In this paper we provide a novel approach to the treatment of granularity in temporal data. A granularity is modeled as a special kind of unanchored temporal primitive that can be used as a unit of time. That is, a granularity is modeled as a unit unanchored temporal primitive. We show how unanchored temporal data is represented, give procedures for converting the data to a given granularity, provide canonical forms for the data, and describe how operations between the data are performed. We also show how anchored temporal data is represented at different granularities and give the semantics of operations on anchored temporal data.
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paper_title: Temporal consistency checking in clinical guidelines acquisition and execution: the GLARE’s approach

paper_content:

GLARE (GuideLine Acquisition, Representation and Execution) is a domain-independent system for the acquisition, representation and execution of clinical guidelines. Temporal constraints play an important role within clinical guidelines (e.g. to specify therapies). The treatment of such constraints is one of the distinguishing features of GLARE. During acquisition, GLARE supports (i) the representation and (ii) the checking of the consistency of the temporal constraints. Moreover, it (iii) automatically checks that the times of execution of specific actions respect the general temporal constraints described in the guideline. This treatment of temporal constraints involves the extension of various Artificial Intelligence (AI) techniques.
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paper_title: Asbru: a task-specific, intention-based, and time-oriented language for representing skeletal plans

paper_content:

Skeletal plans are a powerful way to reuse existing domain-specific procedural knowledge. They are instantiated and refined dynamically over time. In the Asgaard project, we are investigating a set of tasks that support the design and the execution of skeletal plans by a human executing agent other than the original plan designer. The underlying requirement to develop task-specific problem-solving methods is a modeling language. We developed a time-oriented machine-readable language, called Asbru, to represent and to annotate durative skeletal plans based on the task-specific ontology. During the design phase of plans, Asbru allows to express durative actions and plans caused by durative states of an observed agent. We represent explicitly the intentions underlying these plans as temporal patterns to be maintained, achieved or avoided. The result is an uniformly represented and organized plan-specification library. During the execution phase an applicable plan is instantiated with distinctive arguments and state-transition criteria are added to execute and reason about different tasks.
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paper_title: A framework for knowledge-based temporal abstraction

paper_content:

A new domain-independent knowledge-based inference structure is presented, specific to the task of abstracting higher-level concepts from time-stamped data. The framework includes a model of time, parameters, events and contexts. A formal specification of a domain's temporal abstraction knowledge supports acquisition, maintenance, reuse and sharing of that knowledge. ::: ::: The knowledge-based temporal abstraction method decomposes the temporal abstraction task into five subtasks. These subtasks are solved by five domain-independent temporal abstraction mechanisms. The temporal abstraction mechanisms depend on four domain-specific knowledge types: structural, classification (functional), temporal semantic (logical) and temporal dynamic (probabilistic) knowledge. Domain values for all knowledge types are specified when a temporal abstraction system is developed. ::: ::: The knowledge-based temporal abstraction method has been implemented in the RESUME system and has been evaluated in several clinical domains (protocol-based care, monitoring of children's growth and therapy of diabetes) and in an engineering domain (monitoring of traffic control), with encouraging results.
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paper_title: The GLARE approach to clinical guidelines: main features.

paper_content:

In this paper, we present GLARE, a domain-independent prototypical system for acquiring, representing and executing clinical guidelines. GLARE has been built within a 7-year project with Azienda Ospedaliera San Giovanni Battista in Turin (one of the largest hospitals in Italy) and has been successfully tested on clinical guidelines in different domains, including bladder cancer, reflux esophagitis, and heart failure. GLARE is characterized by the adoption of advanced Artificial Intelligence (AI) techniques, to support medical decision making and to manage temporal knowledge.
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paper_title: Towards a comprehensive treatment of temporal constraints in clinical guidelines

paper_content:

In this paper, we focus on an application and extension of artificial intelligence temporal reasoning techniques in order to represent and reason with temporal constraints in clinical guidelines. Particular attention is dedicated to the treatment of repeated (periodic) events, which play a major role in clinical therapies. We also discuss some limitations of our current approach, highlighting possible future enhancements. The work in this paper has been developed in the GLARE project, meant to realize a prototype of a domain-independent manager of clinical guidelines. The GLARE system has been built in cooperation with Azienda Ospedaliera S. Giovanni Battista of Turin, and has been successfully tested on different clinical domains.
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paper_title: A temporal query system for protocol-directed decision support.

paper_content:

Chronus is a query system that supports temporal extensions to the Structured Query Language (SQL) for relational databases. Although the relational data model can store time-stamped data and can permit simple temporal-comparison operations, it does not provide either a closed or a sufficient algebra for manipulating temporal data. In this paper, we outline an algebra that maintains a consistent relational representation of temporal data and that allows the type of temporal queries needed for protocol-directed decision support. We also discuss how Chronus can translate between our temporal algebra and the relational algebra used for SQL queries. We have applied our system to the task of screening patients for clinical trials. Our results demonstrate that Chronous can express sufficiently all required temporal queries, and that the search time of such queries is similar to that of standard SQL.
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paper_title: The Asgaard Project : A Task-Specific Framework for the Application and Critiquing of Time-Oriented Clinical Guidelines

paper_content:

Abstract Clinical guidelines can be viewed as generic skeletal-plan schemata that represent clinical procedural knowledge and that are instantiated and refined dynamically by care providers over significant time periods. In the Asgaard project, we are investigating a set of tasks that support the application of clinical guidelines by a care provider other than the guideline's designer. We are focusing on the application of the guideline, recognition of care providers' intentions from their actions, and critique of care providers' actions given the guideline and the patient's medical record. We are developing methods that perform these tasks in multiple clinical domains, given an instance of a properly represented clinical guideline and an electronic medical patient record. In this paper, we point out the precise domain-specific knowledge required by each method, such as the explicit intentions of the guideline designer (represented as temporal patterns to be achieved or avoided). We present a machine-readable language, called Asbru, to represent and to annotate guidelines based on the task-specific ontology. We also introduce an automated tool for the acquisition of clinical guidelines based on the same ontology, developed using the PROTEGE-II framework.
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paper_title: Temporal Constraint Networks

paper_content:

Abstract This paper extends network-based methods of constraint satisfaction to include continuous variables, thus providing a framework for processing temporal constraints. In this framework, called temporal constraint satisfaction problem (TCSP), variables represent time points and temporal information is represented by a set of unary and binary constraints, each specifying a set of permitted intervals. The unique feature of this framework lies in permitting the processing of metric information, namely, assessments of time differences between events. We present algorithms for performing the following reasoning tasks: finding all feasible times that a given event can occur, finding all possible relationships between two given events, and generating one or more scenarios consistent with the information provided. We distinguish between simple temporal problems (STPs) and general temporal problems, the former admitting at most one interval constraint on any pair of time points. We show that the STP, which subsumes the major part of Vilain and Kautz's point algebra, can be solved in polynomial time. For general TCSPs, we present a decomposition scheme that performs the three reasoning tasks considered, and introduce a variety of techniques for improving its efficiency. We also study the applicability of path consistency algorithms as preprocessing of temporal problems, demonstrate their termination and bound their complexities.
---
paper_title: TSMI: a CEN/TC251 standard for time specific problems in healthcare informatics and telematics

paper_content:

Abstract Time is the most important variable in healthcare, and standards are needed about how to represent information with explicit references to time. In this paper, the European Prestandard `TSMI: time standards for healthcare specific problems' (CEN/TC251 preENV 12381) is presented which aims to be the first contribution to this harmonisation process, focusing on `representation' and `explicit reference' of temporal information in healthcare. The prestandard is mainly composed of two parts. First, the basic building blocks for modelling time-related information are introduced, and a formal representation scheme proposed. In a second part, conformance rules and principles for Healthcare Data and Information as well as for Healthcare Information Systems, are covered.
---
paper_title: Natural Language and Text Processing in Biomedicine

paper_content:

After reading this chapter, you should know the answers to these questions: ::: ::: ::: Why is natural language processing (NLP) important? ::: ::: ::: What are the potential uses for NLP in the biomedical domain? ::: ::: ::: What forms of knowledge are used in NLP? ::: ::: ::: What are the principal techniques of NLP? ::: ::: ::: What are the challenges for NLP in the clinical domain? ::: ::: ::: What are the challenges for NLP in the biological domain?
---
paper_title: A temporal constraint structure for extracting temporal information from clinical narrative

paper_content:

Introduction. Time is an essential element in medical data and knowledge which is intrinsically connected with medical reasoning tasks. Many temporal reasoning mechanisms use constraint-based approaches. Our previous research demonstrates that electronic discharge summaries can be modeled as a simple temporal problem (STP).Objective. To categorize temporal expressions in clinical narrative text and to propose and evaluate a temporal constraint structure designed to model this temporal information and to support the implementation of higher-level temporal reasoning.Methods. A corpus of 200 random discharge summaries across 18 years was applied in a grounded approach to construct a representation structure. Then, a subset of 100 discharge summaries was used to tally the frequency of each identified time category and the percentage of temporal expressions modeled by the structure. Fifty random expressions were used to assess inter-coder agreement.Results. Six main categories of temporal expressions were identified. The constructed temporal constraint structure models time over which an event occurs by constraining its starting time and ending time. It includes a set of fields for the endpoint(s) of an event, anchor information, qualitative and metric temporal relations, and vagueness. In 100 discharge summaries, 1961 of 2022 (97%) identified temporal expressions were effectively modeled using the temporal constraint structure. Inter-coder evaluation of 50 expressions yielded exact match in 90%, partial match with trivial differences in 8%, partial match with large differences in 2%, and total mismatch in 0%.Conclusion. The proposed temporal constraint structure embodies a sufficient and successful implementation method to encode the diversity of temporal information in discharge summaries. Placing data within the structure provides a foundational representation upon which further reasoning, including the addition of domain knowledge and other post-processing to implement an STP, can be accomplished.
---
paper_title: System Architecture for Temporal Information Extraction, Representationand Reasoning in Clinical Narrative Reports

paper_content:

Exploring temporal information in narrative Electronic Medical Records (EMRs) is essential and challenging. We propose an architecture for an integrated approach to process temporal information in clinical narrative reports. The goal is to initiate and build a foundation that supports applications which assist healthcare practice and research by including the ability to determine the time of clinical events (e.g., past vs. present). Key components include: (1) a temporal constraint structure for temporal expressions and the development of an associated tagger; (2) a Natural Language Processing (NLP) system for encoding and extracting medical events and associating them with formalized temporal data; (3) a post-processor, with a knowledge-based subsystem to help discover implicit information, that resolves temporal expressions and deals with issues such as granularity and vagueness; and (4) a reasoning mechanism which models clinical reports as Simple Temporal Problems (STPs).
---
paper_title: Natural Language and Text Processing in Biomedicine

paper_content:

After reading this chapter, you should know the answers to these questions: ::: ::: ::: Why is natural language processing (NLP) important? ::: ::: ::: What are the potential uses for NLP in the biomedical domain? ::: ::: ::: What forms of knowledge are used in NLP? ::: ::: ::: What are the principal techniques of NLP? ::: ::: ::: What are the challenges for NLP in the clinical domain? ::: ::: ::: What are the challenges for NLP in the biological domain?
---
paper_title: Maintaining knowledge about temporal intervals

paper_content:

An interval-based temporal logic is introduced, together with a computationally effective reasoning algorithm based on constraint propagation. This system is notable in offering a delicate balance between
---
paper_title: TSMI: a CEN/TC251 standard for time specific problems in healthcare informatics and telematics

paper_content:

Abstract Time is the most important variable in healthcare, and standards are needed about how to represent information with explicit references to time. In this paper, the European Prestandard `TSMI: time standards for healthcare specific problems' (CEN/TC251 preENV 12381) is presented which aims to be the first contribution to this harmonisation process, focusing on `representation' and `explicit reference' of temporal information in healthcare. The prestandard is mainly composed of two parts. First, the basic building blocks for modelling time-related information are introduced, and a formal representation scheme proposed. In a second part, conformance rules and principles for Healthcare Data and Information as well as for Healthcare Information Systems, are covered.
---
paper_title: Model Formulation: Modeling Electronic Discharge Summaries as a Simple Temporal Constraint Satisfaction Problem

paper_content:

OBJECTIVE ::: To model the temporal information contained in medical narrative reports as a simple temporal constraint satisfaction problem. ::: ::: ::: DESIGN ::: A constraint satisfaction problem is defined by time points and constraints (inequalities between points). A time interval comprises a pair of points and a constraint. Five complete electronic discharge summaries and paragraphs from 226 other discharge summaries were studied. Medical events were represented as intervals, and assertions about events were represented as constraints. Through a consensus process, a set of encoding procedures and a list of issues related to encoding were generated. ::: ::: ::: MEASUREMENTS ::: Instances of temporal disjunction and contradiction and distribution of temporal constraints were used. ::: ::: ::: RESULTS ::: An average of 95 medical events (range, 46-151) and 234 temporal assertions (range, 118-388) were identified per complete discharge summary. Nondefinitional assertions were explicit (36%) or implicit (64%) and absolute (17%), qualitative (72%), or metric (11%). Implicit assertions were based on domain knowledge and assumptions, e.g., the section of the report determined the ordering of events. Issues included linking events, intermittence, periodicity, granularity, vagueness, ambiguity, uncertainty, and plans. ions such as intermittence were not represented explicitly. The temporal network was sparse: Only 0.80% (range, 0.42%-1.38%) of possible constraints were instantiated. No instances of discontinuous temporal disjunction were found in the complete summaries or the 226 paragraphs. One instance of temporal contradiction was found (intrareport rate of 0.2 with a 95% confidence interval of 0.005-1.114). ::: ::: ::: CONCLUSION ::: A simple temporal constraint satisfaction problem appears sufficient to represent most temporal assertions in discharge summaries and may be useful for encoding electronic medical records.
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paper_title: Text Structures In Medical Text Processing: Empirical Evidence And A Text Understanding Prototype

paper_content:

Abstract ::: We consider the role of textual structures in medical texts. In particular, we examine the impact the lacking recognition of text phenomena has on the validity of medical knowledge bases fed by a natural language understanding front-end. First, we review the results from an empirical study on a sample of medical texts considering, in various forms of local coherence phenomena (anaphora and textual ellipses). We then discuss the representation bias emerging in the text knowledge base that is likely to occur when these phenomena are not dealt with--mainly the emergence of referentially incoherent and invalid representations. We then turn to a medical text understanding system designed to account for local text coherence.
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paper_title: Why Discourse Structures in Medical Reports Matter for the Validity of Automatically Generated Text Knowledge Bases

paper_content:

The automatic analysis of medical full-texts currently suffers from neglecting text coherence phenomena such as reference relations between discourse units. This has unwarranted effects on the description adequacy of medical knowledge bases automatically generated from texts. The resulting representation bias can be characterized in terms of artificially fragmented, incomplete and invalid knowledge structures. We discuss three types of textual phenomena (pronominal and nominal anaphora, as well as textual ellipsis) and outline basic methodologies how to deal with them.
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paper_title: Managing and displaying different time granularities of clinical information.

paper_content:

We approach the need of representing by powerful graphical user-interface the granularity of the temporal clinical information. In this work we present some contributions, to suitably display such temporal clinical information. The graphical representation relies on a temporal clinical data model able to manage data having different granularities.
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paper_title: Representing and Reasoning about Temporal Granularities

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In this paper, we propose a new logical approach to represent and to reason about different time granularities. We identify a time granularity as an infinite sequence of time points properly labelled with proposition symbols marking the starting and ending points of the corresponding granules, and we symbolically model sets of granularities by means of linear time logic formulas. Some real-world granularities are provided, from a clinical domain and from the Gregorian Calendar, to motivate and exemplify our approach. Different formulas are introduced, which represent relations between different granularities. The proposed framework permits one to algorithmically solve the consistency, the equivalence, and the classification problems in a uniform way, by reducing them to the validity problem for the considered linear time logic.
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paper_title: Temporal Granularity: Completing the Puzzle

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Granularity is an integral feature of both anchored (e.g., 25 October 1995, July 1996) and unanchored (e.g., 3 minutes, 6 hours 20 minutes, 5 days) temporal data. In supporting temporal data that is specified in different granularities, numerous approaches have been proposed to deal with the issues of converting temporal data from one granularity to another. The emphasis, however, has only been on granularity conversions with respect to anchored temporal data. In this paper we provide a novel approach to the treatment of granularity in temporal data. A granularity is modeled as a special kind of unanchored temporal primitive that can be used as a unit of time. That is, a granularity is modeled as a unit unanchored temporal primitive. We show how unanchored temporal data is represented, give procedures for converting the data to a given granularity, provide canonical forms for the data, and describe how operations between the data are performed. We also show how anchored temporal data is represented at different granularities and give the semantics of operations on anchored temporal data.
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paper_title: Time is Everything: Time-Oriented Clinical Information Systems

paper_content:

Abstract ::: Time is important in clinical information systems. Representing, maintaining, querying, and reasoning about time-oriented clinical data is a major theoretical and practical research area in medical informatics. In this nonexhaustive overview, we present a brief synopsis of research efforts in designing and developing time-oriented information systems in medicine. These efforts can be viewed from either an application point of view, distinguishing between different clinical tasks (such as diagnosis versus therapy) and clinical areas (such as infectious diseases versus oncology), or a methodological point of view, distinguishing between different theoretical approaches. We also explore the two primary methodological and theoretical paths research has taken in the past decade: temporal reasoning and temporal data maintenance. Both of these research areas include efforts to model time, temporal entities, and temporal queries. Collaboration between the two areas is possible, through tasks such as the abstraction of raw time-oriented clinical data into higher-level meaningful clinical concepts and the management of different levels of temporal granularity. Such collaboration could provide a common ground and useful areas for future research and development. We conclude with our view of future research directions.
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paper_title: The algebra IA fuz : a framework for qualitative fuzzy temporal reasoning

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The aim of this work is to integrate the ideas of flexibility and uncertainty into Allen's interval-based temporal framework, defining a new formalism, called IA^f^u^z, which extends classical Interval Algebra (IA), in order to express qualitative fuzzy constraints between intervals. We generalize the classical operations between IA-relations to IA^f^u^z-relations, as well as the concepts of minimality and local consistency, referring to the framework of Fuzzy Constraint Satisfaction Problem. We analyze the most interesting reasoning tasks in our framework, which generalize the classical problems of checking consistency, finding a solution and computing the minimal network in the context of IA. In order to solve these tasks, we devise two constraint propagation algorithms and a Branch & Bound algorithm. Since these tasks are NP-difficult, we address the problem of finding tractable sub-algebras of IA^f^u^z, by extending to our fuzzy framework the classical pointizable sub-algebras SA"c and SA, as well as the maximal tractable subalgebra H introduced by Nebel. In particular, we prove that the fuzzy extension of the latter, called H^f^u^z, shares with its classical counterpart a maximality property, in that it is the unique maximal subalgebra of IA^f^u^z which contains the fuzzy extensions of Allen's atomic relations. ions.
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paper_title: Integrating quantitative and qualitative fuzzy temporal constraints

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In this work we address the problem of representing and reasoning with temporal knowledge in a very general and flexible manner. To this aim we propose a model of integration of quantitative and qualitative temporal information affected by vagueness and uncertainty. We extend our fuzzy qualitative temporal framework IA fuz integrating the treatment of fuzzy quantitative constraints modeled as trapezoidal distributions. To do this, we extend the treatment of fuzzy temporal constraints considered in the literature and we generalize in a fuzzy direction the classical hybrid approach of temporal constraints integration proposed by Meiri. To show the full expressiveness of the new system, we apply it to represent the fuzzy temporal knowledge in a typical scheduling example.
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paper_title: Fuzziness and uncertainty in temporal reasoning

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This paper proposes a general discussion of the handling of imprecise and uncertain information in temporal reasoning in the framework of fuzzy sets and possibility theory. The introduction of fuzzy features in temporal reasoning can be related to different issues. First, it can be motivated by the need of a gradual, linguistic-like description of temporal relations even in the face of complete information. An extension of Allen relational calculus is proposed, based on fuzzy comparators expressing linguistic tolerance. Fuzzy Allen relations are defined from a fuzzy partition made by three possible fuzzy relations between dates (approximately equal, clearly smaller, and clearly greater). Second, the handling of fuzzy or incomplete information leads to pervade classical Allen relations, and more generally fuzzy Allen relations, with uncertainty. The paper provides a detailed presentation of the calculus of fuzzy Allen relations (including the composition table of these relations). Moreover, the paper discusses the patterns for propagating uncertainty about (fuzzy) Allen relations in a possibilistic way.
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paper_title: Temporal Reasoning Based on Semi-Intervals

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Abstract A generalization of Allen's interval-based approach to temporal reasoning is presented. The notion of ‘conceptual neighborhood’ of qualitative relations between events is central to the presented approach. Relations between semi-intervals rather than intervals are used as the basic units of knowledge. Semi-intervals correspond to temporal beginnings or endings of events. We demonstrate the advantages of reasoning on the basis of semi-intervals: (1) semi-intervals are rather natural entities both from a cognitive and from a computational point of view; (2) coarse knowledge can be processed directly; computational effort is saved; (3) incomplete knowledge about events can be fully exploited; (4) incomplete inferences made on the basis of complete knowledge can be used directly for further inference steps; (5) there is no trade-off in computational strength for the added flexibility and efficiency; (6) for a natural subset of Allen's algebra, global consistency can be guaranteed in polynomial time; (7) knowledge about relations between events can be represented much more compactly.
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paper_title: Temporal representation and reasoning in artificial intelligence: A review

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The explicit representation and reasoning about time is an important problem in many areas of artificial intelligence. Over the last 10-15 years, it has been attracting the attention of many researchers. Several temporal reasoning systems, differing in design issues related to ontology of time, underlying temporal logic, temporal constraints used and algorithms employed, have been developed. In this survey, important representational issues which determine a temporal reasoning system are introduced. In particular, several important notions like change, causality, actions are described in terms of time. For each issue different choices available in the literature are discussed. The most influential approaches to temporal reasoning in artificial intelligence are analyzed in terms of these major representational issues.
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paper_title: Temporal Reasoning and Temporal Data Maintenance in Medicine: Issues and Challenges

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Abstract We present a brief, nonexhaustive overview of research efforts in designing and developing time-oriented systems in medieine. The growing volume of research on time-oriented systems in medicine can be viewed from either an application point of view, focusing on different generic tasks (e.g. diagnosis) and clinical areas (e.g. cardiology), or from a methodological point of view, distinguishing between different theoretical approaches. In this overview, we focus on highlighting methodological and theoretical choices, and conclude with suggestions for new research directions. Two main research directions can be noted: temporal reasoning, which supports various temporal inference tasks (e.g. temporal abstraction, time-oriented decision support, forecasting, data validation), and temporal data maintenance, which deals with storage and retrieval of data that have heterogeneous temporal dimensions. Efforts common to both research areas include the modeling of time, of temporal entities, and of temporal queries. We suggest that tasks such as abstraction of time-oriented data and the handling of different temporal-granularity levels should provide common ground for collaboration between the two research directions and fruitful areas for future research.
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paper_title: A temporal constraint structure for extracting temporal information from clinical narrative

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Introduction. Time is an essential element in medical data and knowledge which is intrinsically connected with medical reasoning tasks. Many temporal reasoning mechanisms use constraint-based approaches. Our previous research demonstrates that electronic discharge summaries can be modeled as a simple temporal problem (STP).Objective. To categorize temporal expressions in clinical narrative text and to propose and evaluate a temporal constraint structure designed to model this temporal information and to support the implementation of higher-level temporal reasoning.Methods. A corpus of 200 random discharge summaries across 18 years was applied in a grounded approach to construct a representation structure. Then, a subset of 100 discharge summaries was used to tally the frequency of each identified time category and the percentage of temporal expressions modeled by the structure. Fifty random expressions were used to assess inter-coder agreement.Results. Six main categories of temporal expressions were identified. The constructed temporal constraint structure models time over which an event occurs by constraining its starting time and ending time. It includes a set of fields for the endpoint(s) of an event, anchor information, qualitative and metric temporal relations, and vagueness. In 100 discharge summaries, 1961 of 2022 (97%) identified temporal expressions were effectively modeled using the temporal constraint structure. Inter-coder evaluation of 50 expressions yielded exact match in 90%, partial match with trivial differences in 8%, partial match with large differences in 2%, and total mismatch in 0%.Conclusion. The proposed temporal constraint structure embodies a sufficient and successful implementation method to encode the diversity of temporal information in discharge summaries. Placing data within the structure provides a foundational representation upon which further reasoning, including the addition of domain knowledge and other post-processing to implement an STP, can be accomplished.
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paper_title: Temporal reasoning for decision support in medicine

paper_content:

Objective: Handling time-related concepts is essential in medicine. During diagnosis it can make a substantial difference to know the temporal order in which some symptoms occurred or for how long they lasted. During prognosis the potential evolutions of a disease are conceived as a description of events unfolding in time. In therapy planning the different steps of treatment must be applied in a precise order, with a given frequency and for a certain span of time in order to be effective. This article offers a survey on the use of temporal reasoning for decision support-related tasks in medicine.Material and methods: Key publications of the area, mainly circumscribed to the latest two decades, are reviewed and classified according to three important stages of patient treatment requiring decision support: diagnosis, prognosis and therapy planning/management. Other complementary publications, like those on time-centered information storage and retrieval, are also considered as they provide valuable support to the above mentioned three stages.Results: Key areas are highlighted and used to organize the latest contributions. The survey of previous research is followed by an analysis of what can still be improved and what is needed to make the next generation of decision support systems for medicine more effective.Conclusions: It can be observed that although the area has been considerably developed, there are still areas where more research is needed to make time-based systems of widespread use in decision support-related areas of medicine. Several suggestions for further exploration are proposed as a result of the survey.
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paper_title: Model Formulation: Modeling Electronic Discharge Summaries as a Simple Temporal Constraint Satisfaction Problem

paper_content:

OBJECTIVE ::: To model the temporal information contained in medical narrative reports as a simple temporal constraint satisfaction problem. ::: ::: ::: DESIGN ::: A constraint satisfaction problem is defined by time points and constraints (inequalities between points). A time interval comprises a pair of points and a constraint. Five complete electronic discharge summaries and paragraphs from 226 other discharge summaries were studied. Medical events were represented as intervals, and assertions about events were represented as constraints. Through a consensus process, a set of encoding procedures and a list of issues related to encoding were generated. ::: ::: ::: MEASUREMENTS ::: Instances of temporal disjunction and contradiction and distribution of temporal constraints were used. ::: ::: ::: RESULTS ::: An average of 95 medical events (range, 46-151) and 234 temporal assertions (range, 118-388) were identified per complete discharge summary. Nondefinitional assertions were explicit (36%) or implicit (64%) and absolute (17%), qualitative (72%), or metric (11%). Implicit assertions were based on domain knowledge and assumptions, e.g., the section of the report determined the ordering of events. Issues included linking events, intermittence, periodicity, granularity, vagueness, ambiguity, uncertainty, and plans. ions such as intermittence were not represented explicitly. The temporal network was sparse: Only 0.80% (range, 0.42%-1.38%) of possible constraints were instantiated. No instances of discontinuous temporal disjunction were found in the complete summaries or the 226 paragraphs. One instance of temporal contradiction was found (intrareport rate of 0.2 with a 95% confidence interval of 0.005-1.114). ::: ::: ::: CONCLUSION ::: A simple temporal constraint satisfaction problem appears sufficient to represent most temporal assertions in discharge summaries and may be useful for encoding electronic medical records.
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paper_title: medSynDiKATe—a natural language system for the extraction of medical information from findings reports

paper_content:

Abstract medSyn D iKATe is a natural language processor, which automatically acquires medical information from findings reports. In the course of text analysis their contents is transferred to conceptual representation structures, which constitute a corresponding text knowledge base. medSyn D iKATe is particularly adapted to deal properly with text structures, such as various forms of anaphoric reference relations spanning several sentences. The strong demands medSyn D iKATe poses on the availability of expressive knowledge sources are accounted for by two alternative approaches to acquire medical domain knowledge (semi)automatically. We also present data for the information extraction performance of medSyn D iKATe in terms of the semantic interpretation of three major syntactic patterns in medical documents.
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paper_title: Retrieving time information from natural-language texts

paper_content:

An understanding of time relations is central to processing information contained in a narrative. A typical narrative is concerned with the relative ordering or progression of events over time, and the information that one would like to retrieve is often of the type 'What happened after event x?' or 'Did event x precede event y?' Determination of causality also requires a knowledge of time relations, since an event x can cause an event y only if it precedes event y in time. There has been considerable interest in the processing of time information among researchers in artificial intelligence. Several systems have been designed which compute time relations from a structured input of time specifications. Kahn and Gorry (1977) present a 'time specialist' program which accepts input about time relations in a LISP-like form; from this input, the 'specialist' comp~tes time relations, checks for inconsistencies in the input and answers questic~ns about time relations between the events given in the input. The speciali~' provides several different ways of organising the time
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paper_title: Coherence and Coreference*

paper_content:

Coherence in conversations and in texts can be partially characterized by a set of coherence relations, motivated ultimately by the speaker's or writer's need to be understood. In this paper, formal definitions are given for several coherence relations, based on the operations of an inference system; that is, the relations between successive portions of a discourse are characterized in terms of the inferences that can be drawn from each. In analyzing a discourse, it is frequently the case that we would recognize it as coherent, in that it would satisfy the formal definition of some coherence relation, if only we could assume certain noun phrases to be coreferential. In such cases, we will simply assume the identity of the entities referred to, in what might be called a “petty conversational implicature,” thereby solving the coherence and coreference problems simultaneously. Three examples of different kinds of reference problems are presented. In each, it is shown how the coherence of the discourse can be recognized, and how the reference problems are solved, almost as a by-product, by means of these petty conversational implicatures.
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paper_title: Reference standards, judges, and comparison subjects: roles for experts in evaluating system performance

paper_content:

Medical informatics systems are often designed to perform at the level of human experts. Evaluation of the performance of these systems is often constrained by lack of reference standards, either because the appropriate response is not known or because no simple appropriate response exists. Even when performance can be assessed, it is not always clear whether the performance is sufficient or reasonable. These challenges can be addressed if an evaluator enlists the help of clinical domain experts. 1) The experts can carry out the same tasks as the system, and then their responses can be combined to generate a reference standard. 2)The experts can judge the appropriateness of system output directly. 3) The experts can serve as comparison subjects with which the system can be compared. These are separate roles that have different implications for study design, metrics, and issues of reliability and validity. Diagrams help delineate the roles of experts in complex study designs.
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paper_title: Evaluating Natural Language Processors in the Clinical Domain

paper_content:

Evaluating natural language processing (NLP) systems in the clinical domain is a difficult task which is important for advancement of the field. A number of NLP systems have been reported that extract information from free-text clinical reports, but not many of the systems have been evaluated. Those that were evaluated noted good performance measures but the results were often weakened by ineffective evaluation methods. In this paper we describe a set of criteria aimed at improving the quality of NLP evaluation studies. We present an overview of NLP evaluations in the clinical domain and also discuss the Message Understanding Conferences (MUC) [1-4]. Although these conferences constitute a series of NLP evaluation studies performed outside of the clinical domain, some of the results are relevant within medicine. In addition, we discuss a number of factors which contribute to the complexity that is inherent in the task of evaluating natural language systems.
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Title: Methodological Review: Temporal reasoning with medical data-A review with emphasis on medical natural language processing

Section 1: Introduction
Description 1: This section provides an introduction to the topic, emphasizing the importance of temporal information in electronic medical records and the challenges involved in processing this information.

Section 2: Revisiting basic concepts in temporal representation and reasoning
Description 2: This section delves into fundamental questions and theories about the nature of time, outlining various philosophical, physical, and psychological perspectives, and discussing their relevance to artificial intelligence and medical informatics.

Section 3: The categories of time - how should diverse temporal expressions be processed?
Description 3: This section categorizes different types of time (natural, conventional, logical) and discusses the complexities involved in processing diverse temporal expressions in natural language.

Section 4: The structure of time - which structure is suitable for a system?
Description 4: This section explores various conceptual structures of time (linear, branching, circular, parallel) and their applications in medical narrative processing.

Section 5: Instant vs. interval - which is the primitive unit to model time?
Description 5: This section discusses whether time should be modeled based on instants or intervals, drawing on arguments from different research perspectives and their relevance to medical data.

Section 6: Absolute time vs. relative time - can temporal references always be anchored in the time axis?
Description 6: This section differentiates between anchored and unanchored temporal references and addresses the challenges associated with modeling semi-intervals in the medical domain.

Section 7: Events, states, actions, and processes
Description 7: This section defines and distinguishes between critical temporal concepts such as events, states, actions, and processes, and discusses their importance in temporal reasoning with medical data.

Section 8: Overview of temporal reasoning with medical data
Description 8: This section gives an overview of developments in temporal reasoning in medical informatics, documenting the evolution of this field and summarizing current methodologies and applications.

Section 9: Processing temporal information in clinical narrative data
Description 9: This section focuses on the linguistics of processing temporal information in clinical narratives, reviewing previous work and discussing critical methodologies and challenges.

Section 10: Other related work
Description 10: This section introduces additional studies related to processing textual temporal information, including modeling textual temporal expressions in clinical databases and clinical guidelines.

Section 11: Challenges in processing time in clinical narrative data
Description 11: This section lists and describes the major challenges encountered in processing temporal information from clinical narratives, corresponding to different layers of linguistic analysis.

Section 12: Key areas for future research
Description 12: This section identifies limitations and suggests key areas for future research in temporal reasoning with medical data, particularly focusing on enhancing the functionality of MLP systems and integrating narrative data with structured data.

Section 13: Conclusion
Description 13: This section provides a summary of the paper, emphasizing the importance of temporal representation and reasoning in medical informatics and identifying areas deserving further attention.