Natural Language Processing in Biomedicine: A Unified System Architecture Overview
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paper_title: Natural language processing in medicine: an overview.

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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: Natural language processing: an introduction

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Objectives To provide an overview and tutorial of natural language processing (NLP) and modern NLP-system design. Target audience This tutorial targets the medical informatics generalist who has limited acquaintance with the principles behind NLP and/or limited knowledge of the current state of the art. Scope We describe the historical evolution of NLP, and summarize common NLP sub-problems in this extensive field. We then provide a synopsis of selected highlights of medical NLP efforts. After providing a brief description of common machine-learning approaches that are being used for diverse NLP sub-problems, we discuss how modern NLP architectures are designed, with a summary of the Apache Foundation’s Unstructured Information Management Architecture. We finally consider possible future directions for NLP, and reflect on the possible impact of IBM Watson on the medical field.
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paper_title: Natural Language Processing in Health Care and Biomedicine

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After reading this chapter, you should know the answers to these questions: ::: ::: ::: Why is natural language processing important? ::: ::: ::: What are the potential uses for natural language processing (NLP) in the biomedical and health domains? ::: ::: ::: What forms of knowledge are used in NLP? ::: ::: ::: What are the principal techniques of NLP? ::: ::: ::: What are challenges for NLP in the clinical, biological, and health consumer domains?
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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: Semantic Text Parsing for Patient Records

paper_content:

Accessibility to a comprehensive variety of different types of structured patient data is critical to improvement in the health care process, yet most patient information is in the form of narrative text. Semantic methods are needed to interpret and map clinical information to a structured form so that the information will be accessible to other automated applications. This chapter focuses on semantic methods that map narrative patient information to a structured coded form.
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paper_title: Natural language processing: State of the art and prospects for significant progress, a workshop sponsored by the National Library of Medicine

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Graphical abstractDisplay Omitted We report on a natural language workshop sponsored by the National Library of Medicine.We summarize the current state of the art in biomedical natural language processing.We report on research strategies for advancing natural language processing.We summarize discussions of obstacles and challenges to the field. Natural language processing (NLP) is crucial for advancing healthcare because it is needed to transform relevant information locked in text into structured data that can be used by computer processes aimed at improving patient care and advancing medicine. In light of the importance of NLP to health, the National Library of Medicine (NLM) recently sponsored a workshop to review the state of the art in NLP focusing on text in English, both in biomedicine and in the general language domain. Specific goals of the NLM-sponsored workshop were to identify the current state of the art, grand challenges and specific roadblocks, and to identify effective use and best practices. This paper reports on the main outcomes of the workshop, including an overview of the state of the art, strategies for advancing the field, and obstacles that need to be addressed, resulting in recommendations for a research agenda intended to advance the field.
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paper_title: Natural language processing: State of the art and prospects for significant progress, a workshop sponsored by the National Library of Medicine

paper_content:

Graphical abstractDisplay Omitted We report on a natural language workshop sponsored by the National Library of Medicine.We summarize the current state of the art in biomedical natural language processing.We report on research strategies for advancing natural language processing.We summarize discussions of obstacles and challenges to the field. Natural language processing (NLP) is crucial for advancing healthcare because it is needed to transform relevant information locked in text into structured data that can be used by computer processes aimed at improving patient care and advancing medicine. In light of the importance of NLP to health, the National Library of Medicine (NLM) recently sponsored a workshop to review the state of the art in NLP focusing on text in English, both in biomedicine and in the general language domain. Specific goals of the NLM-sponsored workshop were to identify the current state of the art, grand challenges and specific roadblocks, and to identify effective use and best practices. This paper reports on the main outcomes of the workshop, including an overview of the state of the art, strategies for advancing the field, and obstacles that need to be addressed, resulting in recommendations for a research agenda intended to advance the field.
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paper_title: Lexical methods for managing variation in biomedical terminologies.

paper_content:

Abstract ::: Access to biomedical terminologies is hampered by the high degree of variability inherent in natural language terms and in the terminologies themselves. The lexicon, lexical programs, databases, and indexes included with the 1994 release of the UMLS Knowledge Sources are designed to help users manage this variability. We describe these resources and illustrate their flexibility and usefulness in providing enhanced access to data in the UMLS Metathesaurus.
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paper_title: Semantic Text Parsing for Patient Records

paper_content:

Accessibility to a comprehensive variety of different types of structured patient data is critical to improvement in the health care process, yet most patient information is in the form of narrative text. Semantic methods are needed to interpret and map clinical information to a structured form so that the information will be accessible to other automated applications. This chapter focuses on semantic methods that map narrative patient information to a structured coded form.
---
paper_title: Natural language processing: State of the art and prospects for significant progress, a workshop sponsored by the National Library of Medicine

paper_content:

Graphical abstractDisplay Omitted We report on a natural language workshop sponsored by the National Library of Medicine.We summarize the current state of the art in biomedical natural language processing.We report on research strategies for advancing natural language processing.We summarize discussions of obstacles and challenges to the field. Natural language processing (NLP) is crucial for advancing healthcare because it is needed to transform relevant information locked in text into structured data that can be used by computer processes aimed at improving patient care and advancing medicine. In light of the importance of NLP to health, the National Library of Medicine (NLM) recently sponsored a workshop to review the state of the art in NLP focusing on text in English, both in biomedicine and in the general language domain. Specific goals of the NLM-sponsored workshop were to identify the current state of the art, grand challenges and specific roadblocks, and to identify effective use and best practices. This paper reports on the main outcomes of the workshop, including an overview of the state of the art, strategies for advancing the field, and obstacles that need to be addressed, resulting in recommendations for a research agenda intended to advance the field.
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paper_title: Integrating existing natural language processing tools for medication extraction from discharge summaries

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Objective: To develop an automated system to extract medications and related information from discharge summaries as part of the 2009 i2b2 natural language processing (NLP) challenge. This task required accurate recognition of medication name, dosage, mode, frequency, duration, and reason for drug administration. ::: ::: Design: We developed an integrated system using several existing NLP components developed at Vanderbilt University Medical Center, which included MedEx (to extract medication information), SecTag (a section identification system for clinical notes), a sentence splitter, and a spell checker for drug names. Our goal was to achieve good performance with minimal to no specific training for this document corpus; thus, evaluating the portability of those NLP tools beyond their home institution. The integrated system was developed using 17 notes that were annotated by the organizers and evaluated using 251 notes that were annotated by participating teams. ::: ::: Measurements: The i2b2 challenge used standard measures, including precision, recall, and F-measure, to evaluate the performance of participating systems. There were two ways to determine whether an extracted textual finding is correct or not: exact matching or inexact matching. The overall performance for all six types of medication-related findings across 251 annotated notes was considered as the primary metric in the challenge. ::: ::: Results: Our system achieved an overall F-measure of 0.821 for exact matching (0.839 precision; 0.803 recall) and 0.822 for inexact matching (0.866 precision; 0.782 recall). The system ranked second out of 20 participating teams on overall performance at extracting medications and related information. ::: ::: Conclusions: The results show that the existing MedEx system, together with other NLP components, can extract medication information in clinical text from institutions other than the site of algorithm development with reasonable performance.
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paper_title: Natural Language Processing in Health Care and Biomedicine

paper_content:

After reading this chapter, you should know the answers to these questions: ::: ::: ::: Why is natural language processing important? ::: ::: ::: What are the potential uses for natural language processing (NLP) in the biomedical and health domains? ::: ::: ::: What forms of knowledge are used in NLP? ::: ::: ::: What are the principal techniques of NLP? ::: ::: ::: What are challenges for NLP in the clinical, biological, and health consumer domains?
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paper_title: MedEx: a medication information extraction system for clinical narratives.

paper_content:

Medication information is one of the most important types of clinical data in electronic medical records. It is critical for healthcare safety and quality, as well as for clinical research that uses electronic medical record data. However, medication data are often recorded in clinical notes as free-text. As such, they are not accessible to other computerized applications that rely on coded data. We describe a new natural language processing system (MedEx), which extracts medication information from clinical notes. MedEx was initially developed using discharge summaries. An evaluation using a data set of 50 discharge summaries showed it performed well on identifying not only drug names (F-measure 93.2%), but also signature information, such as strength, route, and frequency, with F-measures of 94.5%, 93.9%, and 96.0% respectively. We then applied MedEx unchanged to outpatient clinic visit notes. It performed similarly with F-measures over 90% on a set of 25 clinic visit notes.
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paper_title: Lexical methods for managing variation in biomedical terminologies.

paper_content:

Abstract ::: Access to biomedical terminologies is hampered by the high degree of variability inherent in natural language terms and in the terminologies themselves. The lexicon, lexical programs, databases, and indexes included with the 1994 release of the UMLS Knowledge Sources are designed to help users manage this variability. We describe these resources and illustrate their flexibility and usefulness in providing enhanced access to data in the UMLS Metathesaurus.
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paper_title: Automatic encoding into SNOMED III: a preliminary investigation.

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Abstract ::: The Linguistic String Project (LSP) medical language processing (MLP) system converts narrative clinical reports into database tables of patient data. A procedure for mapping the output of the LSP MLP system into SNOMED III codes was developed. Preliminary results and further requirements are discussed.
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paper_title: Natural Language Processing and the Representation of Clinical Data

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Objective : Develop a representation of clinical observations and actions and a method of processing free-text patient documents to facilitate applications such as quality assurance. ::: ::: Design : The Linguistic String Project (LSP) system of New York University utilizes syntactic analysis, augmented by a sublanguage grammar and an information structure that are specific to the clinical narrative, to map free-text documents into a database for querying. ::: ::: Measurements : Information precision (I-P) and information recall (I-R) were measured for queries for the presence of 13 asthma-health-care quality assurance criteria in a database generated from 59 discharge letters. ::: ::: Results : I-P, using counts of major errors only, was 95.7% for the 28-letter training set and 98.6% for the 31-letter test set. I-R, using counts of major omissions only, was 93.9% for the training set and 92.5% for the test set.
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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: Automatic encoding into SNOMED III: a preliminary investigation.

paper_content:

Abstract ::: The Linguistic String Project (LSP) medical language processing (MLP) system converts narrative clinical reports into database tables of patient data. A procedure for mapping the output of the LSP MLP system into SNOMED III codes was developed. Preliminary results and further requirements are discussed.
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paper_title: A Schema for Representing Medical Language Applied to Clinical Radiology

paper_content:

Objective : Develop a representational schema for clinical concepts and apply it to the task of encoding radiology reports of the chest. ::: ::: Design : The schema was developed following a manual analysis of sample reports from the domain. The schema has two main components: the Medical Entities Dictionary (MED), which specifies the formal representation of the concepts in the domain and of their structures, and the natural-language processor, which specifies the linguistic expressions of the concepts. The schema was evaluated by applying it to a test set of 7,500 reports. Two-hundred reports from the test set were manually analyzed by a medical expert to determine the accuracy and success rate of the system. ::: ::: Results : 82% of the 7,500 reports that contained relevant clinical information were successfully structured automatically. For the smaller set of 200 reports, 80% were structured successfully with an accuracy rate of 97%. ::: ::: Conclusions : The schema is a formal representation for clinical concepts in radiology reports, and provides domain coverage that is particularly well-suited for natural-language processing of radiology for use in a decision support system.
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paper_title: Medical language processing: applications to patient data representation and automatic encoding.

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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: 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 Schema for Representing Medical Language Applied to Clinical Radiology

paper_content:

Objective : Develop a representational schema for clinical concepts and apply it to the task of encoding radiology reports of the chest. ::: ::: Design : The schema was developed following a manual analysis of sample reports from the domain. The schema has two main components: the Medical Entities Dictionary (MED), which specifies the formal representation of the concepts in the domain and of their structures, and the natural-language processor, which specifies the linguistic expressions of the concepts. The schema was evaluated by applying it to a test set of 7,500 reports. Two-hundred reports from the test set were manually analyzed by a medical expert to determine the accuracy and success rate of the system. ::: ::: Results : 82% of the 7,500 reports that contained relevant clinical information were successfully structured automatically. For the smaller set of 200 reports, 80% were structured successfully with an accuracy rate of 97%. ::: ::: Conclusions : The schema is a formal representation for clinical concepts in radiology reports, and provides domain coverage that is particularly well-suited for natural-language processing of radiology for use in a decision support system.
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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: Respiratory Isolation of Tuberculosis Patients Using Clinical Guidelines and an Automated Clinical Decision Support System

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OBJECTIVE: To evaluate a clinical guideline and an automated computer protocol for detection and respiratory isolation of tuberculosis (TB) patients. DESIGN: An automated computer protocol was tested on a retrospective cohort of adult culture-positive TB patients admitted from 1992 to 1993 to ColumbiaPresbyterian Medical Center and evaluated prospectively from July 1995 until July 1996. SETTING: A large teaching hospital in New York City. PATIENTS: 171 adult patients admitted from 1992 to 1993 and 43 patients admitted between July 1995 and July 1996. INTERVENTIONS: The 1990 Centers for Disease Control and Prevention guidelines for preventing transmission of TB were adapted to formulate clinical guidelines to ensure early isolation of TB patients at ColumbiaPresbyterian Medical Center. RESULTS: Implementation of a clinical respiratory isolation protocol resulted in a significant improvement in TB patient isolation rates, from 45 (51%) of 88 in 1992 to 62 (75%) of 83 in 1993 (P<.001). In testing automated protocols, the theoretical improvement would have identified an additional 27 patients not isolated by clinicians, making the overall isolation rate 134 (78%) of 171. For the prospective evaluation, 30 (70%) of 43 TB patients were isolated by clinicians adhering to the clinical protocol. Four additional patients were identified by the automated TB protocol, making the combined isolation rate 34 (79%) of 43. CONCLUSIONS: A clinical policy to isolate TB patients and suspected human immunodeficiency virusinfected patients with cough, fever, or radiographic abnormalities improved isolation of culture-documented TB patients from 1992 to 1993. Automated computer protocols were successful in identifying additional potentially infectious patients that clinicians failed to place on respiratory isolation. Clinical and automated protocols combined resulted in better isolation rates than a clinical protocol alone. (Infect Control Hosp Epidemiol 1998;19:94-100). Reports of hospital outbreaks of tuberculosis (TB) at institutions in New York City and Miami have been notable for unusual antimicrobial resistance patterns, atypical clinical presentations, and high mortality among human immunodeficiency virus (HIV)-infected individuals.1-4 Practices and facilities for recognizing and isolating potential TB patients were inadequate during the outbreaks.' The TB infection control program at ColumbiaPresbyterian Medical Center (CPMC) was revised in May 1992 to promote early detection and isolation of potentially infectious TB patients. The policy, adapted from the Centers for Disease Control and Prevention (CDC) guidelines for preventing nosocoFrom the Department of Medicine, College of Physicians and Surgeons, Columbia University (Drs. Knirsch and Pablos-Mendez); Department of Medical Informatics, Columbia University (Drs. Jain, Friedman, and Hripcsak); the Bureau of Tuberculosis Control, New York City Department of Health (Drs. Knirsch and Pablos-Mendez); the Department of Computer Science, Queens College, the City University of New York (Dr Friedman); the Division of Hospital Epidemiology, the Presbyterian Hospital, Columbia-Presbyterian Medical Center (Dr Knirsch) , New York City, New York. The authors thank Steven Shea, Paul Clayton, Jay Dobkin, and Thomas Frieden for their advice, direction, and support. Supported by grant 36-40-94065 from Telecommunications and Information Infrastructure Assistance Program, National Telecommunications and Information Administration, US Department of Commerce; grants T15-LM07079, R29-LM05627, and R29LM05397 from the National Library of Medicine; Columbia University Center for Advanced Technology in High Performance Computing and Communication in Health Care, a New York State Center for Advanced Technology supported by the New York State Science and Technology Foundation. Address reprint requests to Charles Knirsch, MD, MPH, 630 W 168th St, New York, NY 10032. 96-0A-213. Knirsch CA, Jain NL, Pablos-Mendez A, Friedman C, Hripcsak G. Respiratory isolation of tuberculosis patients using clinical guidelines and an automated clinical decision support system. Infect Control Hosp Epidemiol 1998;19:94-100. This content downloaded from 157.55.39.217 on Mon, 18 Apr 2016 07:53:36 UTC All use subject to http://about.jstor.org/terms Vol. 19 No. 2 ISOLATION OF TUBERCULOSIS PATIENTS 95 mial TB transmission,6 recommends respiratory isolation for all patients suspected to have pulmonary TB or extrapulmonary TB (unless pulmonary TB has been excluded) and for HIV-infected patients with abnormal chest radiographs or cough and fever. The variability of physician practices on the TB and acquired immunodeficiency syndrome (AIDS) service at CPMC and the large volume of chest radiographs and patient admissions makes surveillance of adherence to the clinical respiratory isolation policy difficult. An automated clinical decision support system, which alerts hospital epidemiology about patients who need to be isolated, has been developed to augment the clinical application of the respiratory isolation guidelines. This study describes an evaluation of a clinical protocol from 1992 to 1993 and the subsequent development of an automated clinical decision support system for detecting and isolating patients found to have positive cultures for TB.
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paper_title: Information extraction from full text scientific articles: Where are the keywords?

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BackgroundTo date, many of the methods for information extraction of biological information from scientific articles are restricted to the abstract of the article. However, full text articles in electronic version, which offer larger sources of data, are currently available. Several questions arise as to whether the effort of scanning full text articles is worthy, or whether the information that can be extracted from the different sections of an article can be relevant.ResultsIn this work we addressed those questions showing that the keyword content of the different sections of a standard scientific article (abstract, introduction, methods, results, and discussion) is very heterogeneous.ConclusionsAlthough the abstract contains the best ratio of keywords per total of words, other sections of the article may be a better source of biologically relevant data.
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paper_title: GENIES : a natural-language processing system for the extraction of molecular pathways from journal articles

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Systems that extract structured information from natural language passages have been highly successful in specialized domains. The time is opportune for developing analogous applications for molecular biology and genomics. We present a system, GENIES, that extracts and structures information about cellular pathways from the biological literature in accordance with a knowledge model that we developed earlier. We implemented GENIES by modifying an existing medical natural language processing system, MedLEE, and performed a preliminary evaluation study. Our results demonstrate the value of the underlying techniques for the purpose of acquiring valuable knowledge from biological
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paper_title: A Schema for Representing Medical Language Applied to Clinical Radiology

paper_content:

Objective : Develop a representational schema for clinical concepts and apply it to the task of encoding radiology reports of the chest. ::: ::: Design : The schema was developed following a manual analysis of sample reports from the domain. The schema has two main components: the Medical Entities Dictionary (MED), which specifies the formal representation of the concepts in the domain and of their structures, and the natural-language processor, which specifies the linguistic expressions of the concepts. The schema was evaluated by applying it to a test set of 7,500 reports. Two-hundred reports from the test set were manually analyzed by a medical expert to determine the accuracy and success rate of the system. ::: ::: Results : 82% of the 7,500 reports that contained relevant clinical information were successfully structured automatically. For the smaller set of 200 reports, 80% were structured successfully with an accuracy rate of 97%. ::: ::: Conclusions : The schema is a formal representation for clinical concepts in radiology reports, and provides domain coverage that is particularly well-suited for natural-language processing of radiology for use in a decision support system.
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paper_title: Machine-learned solutions for three stages of clinical information extraction: the state of the art at i2b2 2010

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Objective As clinical text mining continues to mature, its potential as an enabling technology for innovations in patient care and clinical research is becoming a reality. A critical part of that process is rigid benchmark testing of natural language processing methods on realistic clinical narrative. In this paper, the authors describe the design and performance of three state-of-the-art text-mining applications from the National Research Council of Canada on evaluations within the 2010 i2b2 challenge. ::: ::: Design The three systems perform three key steps in clinical information extraction: (1) extraction of medical problems, tests, and treatments, from discharge summaries and progress notes; (2) classification of assertions made on the medical problems; (3) classification of relations between medical concepts. Machine learning systems performed these tasks using large-dimensional bags of features, as derived from both the text itself and from external sources: UMLS, cTAKES, and Medline. ::: ::: Measurements Performance was measured per subtask, using micro-averaged F-scores, as calculated by comparing system annotations with ground-truth annotations on a test set. ::: ::: Results The systems ranked high among all submitted systems in the competition, with the following F-scores: concept extraction 0.8523 (ranked first); assertion detection 0.9362 (ranked first); relationship detection 0.7313 (ranked second). ::: ::: Conclusion for all tasks, we found that the introduction of a wide range of features was crucial to success. Importantly, our choice of machine learning algorithms allowed us to be versatile in our feature design, and to introduce a large number of features without overfitting and without encountering computing-resource bottlenecks.
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paper_title: Automatic identification of pneumonia related concepts on chest x-ray reports

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Abstract ::: A medical language processing system called SymText, two other automated methods, and a lay person were compared against an internal medicine resident for their ability to identify pneumonia related concepts on chest x-ray reports. Sensitivity (recall), specificity, and positive predictive value (precision) are reported with respect to an independent panel of physicians. Overall the performance of SymText was similar to the physician and superior to the other methods. The automatic encoding of pneumonia concepts will support clinical research, decision making, computerized clinical protocols, and quality assurance in a radiology department.
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paper_title: Automatic quality of life prediction using electronic medical records.

paper_content:

Health related quality of life (HRQOL) is an important variable used for prognosis and measuring outcomes in clinical studies and for quality improvement. We explore the use of a general pur-pose natural language processing system Metamap in combination with Support Vector Machines (SVM) for predicting patient responses on standardized HRQOL assessment instruments from text of physicians notes. We surveyed 669 patients in the Mayo Clinic diabetes registry using two instruments designed to assess functioning: EuroQoL5D and SF36/SD6. Clinical notes for these patients were represented as sets of medical concepts using Metamap. SVM classifiers were trained using various feature selection strategies. The best concordance between the HRQOL instruments and automatic classification was achieved along the pain dimension (positive agreement .76, negative agreement .78, kappa .54) using Metamap. We conclude that clinicians notes may be used to develop a surrogate measure of patients HRQOL status.
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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: An overview of MetaMap: historical perspective and recent advances.

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MetaMap is a widely available program providing access to the concepts in the unified medical language system (UMLS) Metathesaurus from biomedical text. This study reports on MetaMap’s evolution over more than a decade, concentrating on those features arising out of the research needs of the biomedical informatics community both within and outside of the National Library of Medicine. Such features include the detection of author-defined acronyms/abbreviations, the ability to browse the Metathesaurus for concepts even tenuously related to input text, the detection of negation in situations in which the polarity of predications is important, word sense disambiguation (WSD), and various technical and algorithmic features. Near-term plans for MetaMap development include the incorporation of chemical name recognition and enhanced WSD.
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paper_title: PhenDisco: phenotype discovery system for the database of genotypes and phenotypes

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The database of genotypes and phenotypes (dbGaP) developed by the National Center for Biotechnology Information (NCBI) is a resource that contains information on various genome-wide association studies (GWAS) and is currently available via NCBI's dbGaP Entrez interface. The database is an important resource, providing GWAS data that can be used for new exploratory research or cross-study validation by authorized users. However, finding studies relevant to a particular phenotype of interest is challenging, as phenotype information is presented in a non-standardized way. To address this issue, we developed PhenDisco (phenotype discoverer), a new information retrieval system for dbGaP. PhenDisco consists of two main components: (1) text processing tools that standardize phenotype variables and study metadata, and (2) information retrieval tools that support queries from users and return ranked results. In a preliminary comparison involving 18 search scenarios, PhenDisco showed promising performance for both unranked and ranked search comparisons with dbGaP's search engine Entrez. The system can be accessed at .
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paper_title: Mayo clinical Text Analysis and Knowledge Extraction System (cTAKES): architecture, component evaluation and applications

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We aim to build and evaluate an open-source natural language processing system for information extraction from electronic medical record clinical free-text. We describe and evaluate our system, the clinical Text Analysis and Knowledge Extraction System (cTAKES), released open-source at . The cTAKES builds on existing open-source technologies—the Unstructured Information Management Architecture framework and OpenNLP natural language processing toolkit. Its components, specifically trained for the clinical domain, create rich linguistic and semantic annotations. Performance of individual components: sentence boundary detector accuracy=0.949; tokenizer accuracy=0.949; part-of-speech tagger accuracy=0.936; shallow parser F-score=0.924; named entity recognizer and system-level evaluation F-score=0.715 for exact and 0.824 for overlapping spans, and accuracy for concept mapping, negation, and status attributes for exact and overlapping spans of 0.957, 0.943, 0.859, and 0.580, 0.939, and 0.839, respectively. Overall performance is discussed against five applications. The cTAKES annotations are the foundation for methods and modules for higher-level semantic processing of clinical free-text.
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paper_title: Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents

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Objective To describe a new medication information extraction systemdTextractorddeveloped for the ‘i2b2 medication extraction challenge’. The development, functionalities, and official evaluation of the system are detailed. Design Textractor is based on the Apache Unstructured Information Management Architecture (UMIA) framework, and uses methods that are a hybrid between machine learning and pattern matching. Two modules in the system are based on machine learning algorithms, while other modules use regular expressions, rules, and dictionaries, and one module embeds MetaMap Transfer. Measurements The official evaluation was based on a reference standard of 251 discharge summaries annotated by all teams participating in the challenge. The metrics used were recall, precision, and the F1-measure. They were calculated with exact and inexact matches, and were averaged at the level of systems and documents. Results The reference metric for this challenge, the system-level overall F1-measure, reached about 77% for exact matches, with a recall of 72% and a precision of 83%. Performance was the best with route information (F1-measure about 86%), and was good for dosage and frequency information, with F1-measures of about 82e85%. Results were not as good for durations, with F1-measures of 36e39%, and for reasons, with F1-measures of 24e27%. Conclusion The official evaluation of Textractor for the i2b2 medication extraction challenge demonstrated satisfactory performance. This system was among the 10 best performing systems in this challenge.
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paper_title: A Simple Algorithm for Identifying Negated Findings and Diseases in Discharge Summaries

paper_content:

Narrative reports in medical records contain a wealth of information that may augment structured data for managing patient information and predicting trends in diseases. Pertinent negatives are evident in text but are not usually indexed in structured databases. The objective of the study reported here was to test a simple algorithm for determining whether a finding or disease mentioned within narrative medical reports is present or absent. We developed a simple regular expression algorithm called NegEx that implements several phrases indicating negation, filters out sentences containing phrases that falsely appear to be negation phrases, and limits the scope of the negation phrases. We compared NegEx against a baseline algorithm that has a limited set of negation phrases and a simpler notion of scope. In a test of 1235 findings and diseases in 1000 sentences taken from discharge summaries indexed by physicians, NegEx had a specificity of 94.5% (versus 85.3% for the baseline), a positive predictive value of 84.5% (versus 68.4% for the baseline) while maintaining a reasonable sensitivity of 77.8% (versus 88.3% for the baseline). We conclude that with little implementation effort a simple regular expression algorithm for determining whether a finding or disease is absent can identify a large portion of the pertinent negatives from discharge summaries.
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paper_title: Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system

paper_content:

BackgroundThe text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research study on airways disease.MethodsThe principal diagnosis, co-morbidity and smoking status extracted by HITEx from a set of 150 discharge summaries were compared to an expert-generated gold standard.ResultsThe accuracy of HITEx was 82% for principal diagnosis, 87% for co-morbidity, and 90% for smoking status extraction, when cases labeled "Insufficient Data" by the gold standard were excluded.ConclusionWe consider the results promising, given the complexity of the discharge summaries and the extraction tasks.
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paper_title: Mayo Clinic NLP System for Patient Smoking Status Identification

paper_content:

This article describes our system entry for the 2006 I2B2 contest "Challenges in Natural Language Processing for Clinical Data" for the task of identifying the smoking status of patients. Our system makes the simplifying assumption that patient-level smoking status determination can be achieved by accurately classifying individual sentences from a patient's record. We created our system with reusable text analysis components built on the Unstructured Information Management Architecture and Weka. This reuse of code minimized the development effort related specifically to our smoking status classifier. We report precision, recall, F-score, and 95% exact confidence intervals for each metric. Recasting the classification task for the sentence level and reusing code from other text analysis projects allowed us to quickly build a classification system that performs with a system F-score of 92.64 based on held-out data tests and of 85.57 on the formal evaluation data. Our general medical natural language engine is easily adaptable to a real-world medical informatics application. Some of the limitations as applied to the use-case are negation detection and temporal resolution.
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paper_title: A natural language parsing system for encoding admitting diagnoses.

paper_content:

Abstract ::: Free-text or natural language documents make up an increasing part of the computerized medical record. While they do provide accessible clinical information to health care personnel, they fail to support processes that require clinical data coded according to a shared lexicon and data structure. We have developed a natural language parser that converts free-text admitting diagnoses into a coded form. This application has proven acceptably accurate in the experimental laboratory to warrant a test in the target clinical environment. Here we describe an approach to moving this research application into a production environment where it can contribute to the efforts of the Health Information Services Department. This transition is essential if the products of natural language understanding research are to contribute to patient care in a routine and sustainable way.
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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: A suite of natural language processing tools developed for the I2B2 project.

paper_content:

Textual medical records contain a wealth of information that needs to be extracted and/or indexed in order to be analyzed and interpreted by the automated tools. We have developed a collection of natural language processing (NLP) tools to extract various types of information from unstructured medical records. The generic NLP components, when assembled in pipelines and initialized with custom configuration parameters, become a powerful medical data mining instrument. We have successfully extracted such medical concepts as diagnoses, comorbidities, discharge medications, and smoking status from various types of medical records.
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paper_title: Mayo clinic smoking status classification system: extensions and improvements.

paper_content:

This paper describes improvements of and extensions to the Mayo Clinic 2006 smoking status classification system. The new system aims at addressing some of the limitations of the previous one. The performance improvements were mainly achieved through remodeling the negation detection for non-smoker, temporal resolution to distinguish a past and current smoker, and improved detection of the smoking status category of unknown. In addition, we introduced a rule-based component for patient-level smoking status assignments in which the individual smoking statuses of all clinical documents for a given patient are aggregated and analyzed to produce the final patient smoking status. The enhanced system builds upon components from Mayo’s clinical Text Analysis and Knowledge Extraction System developed within IBM’s Unstructured Information Management Architecture framework. This reusability minimized the development effort. The extended system is in use to identify smoking status risk factors for a peripheral artery disease NHGRI study.
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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: A natural language understanding system combining syntactic and semantic techniques.

paper_content:

A large proportion of the medical record currently available in computerized medical information systems is in the form of free text reports. While the accessibility of this source of data is improved through inclusion in the computerized record, it remains unavailable for automated decision support, medical research, and management of medical delivery systems. Natural language understanding systems (NLUS) designed to encode free text reports represent one approach to making this information available for these uses. Below we describe an experimental NLUS designed to parse the reports of chest radiographs and store the clinical data extracted in a medical data base.
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paper_title: Information extraction from full text scientific articles: Where are the keywords?

paper_content:

BackgroundTo date, many of the methods for information extraction of biological information from scientific articles are restricted to the abstract of the article. However, full text articles in electronic version, which offer larger sources of data, are currently available. Several questions arise as to whether the effort of scanning full text articles is worthy, or whether the information that can be extracted from the different sections of an article can be relevant.ResultsIn this work we addressed those questions showing that the keyword content of the different sections of a standard scientific article (abstract, introduction, methods, results, and discussion) is very heterogeneous.ConclusionsAlthough the abstract contains the best ratio of keywords per total of words, other sections of the article may be a better source of biologically relevant data.
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paper_title: Effective Mapping of Biomedical Text to the UMLS Metathesaurus: The MetaMap Program

paper_content:

Abstract ::: The UMLS Metathesaurus, the largest thesaurus in the biomedical domain, provides a representation of biomedical knowledge consisting of concepts classified by semantic type and both hierarchical and non-hierarchical relationships among the concepts. This knowledge has proved useful for many applications including decision support systems, management of patient records, information retrieval (IR) and data mining. Gaining effective access to the knowledge is critical to the success of these applications. This paper describes MetaMap, a program developed at the National Library of Medicine (NLM) to map biomedical text to the Metathesaurus or, equivalently, to discover Metathesaurus concepts referred to in text. MetaMap uses a knowledge intensive approach based on symbolic, natural language processing (NLP) and computational linguistic techniques. Besides being applied for both IR and data mining applications, MetaMap is one of the foundations of NLM's Indexing Initiative System which is being applied to both semi-automatic and fully automatic indexing of the biomedical literature at the library.
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paper_title: Automatic quality of life prediction using electronic medical records.

paper_content:

Health related quality of life (HRQOL) is an important variable used for prognosis and measuring outcomes in clinical studies and for quality improvement. We explore the use of a general pur-pose natural language processing system Metamap in combination with Support Vector Machines (SVM) for predicting patient responses on standardized HRQOL assessment instruments from text of physicians notes. We surveyed 669 patients in the Mayo Clinic diabetes registry using two instruments designed to assess functioning: EuroQoL5D and SF36/SD6. Clinical notes for these patients were represented as sets of medical concepts using Metamap. SVM classifiers were trained using various feature selection strategies. The best concordance between the HRQOL instruments and automatic classification was achieved along the pain dimension (positive agreement .76, negative agreement .78, kappa .54) using Metamap. We conclude that clinicians notes may be used to develop a surrogate measure of patients HRQOL status.
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paper_title: An overview of MetaMap: historical perspective and recent advances.

paper_content:

MetaMap is a widely available program providing access to the concepts in the unified medical language system (UMLS) Metathesaurus from biomedical text. This study reports on MetaMap’s evolution over more than a decade, concentrating on those features arising out of the research needs of the biomedical informatics community both within and outside of the National Library of Medicine. Such features include the detection of author-defined acronyms/abbreviations, the ability to browse the Metathesaurus for concepts even tenuously related to input text, the detection of negation in situations in which the polarity of predications is important, word sense disambiguation (WSD), and various technical and algorithmic features. Near-term plans for MetaMap development include the incorporation of chemical name recognition and enhanced WSD.
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paper_title: Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents

paper_content:

Objective To describe a new medication information extraction systemdTextractorddeveloped for the ‘i2b2 medication extraction challenge’. The development, functionalities, and official evaluation of the system are detailed. Design Textractor is based on the Apache Unstructured Information Management Architecture (UMIA) framework, and uses methods that are a hybrid between machine learning and pattern matching. Two modules in the system are based on machine learning algorithms, while other modules use regular expressions, rules, and dictionaries, and one module embeds MetaMap Transfer. Measurements The official evaluation was based on a reference standard of 251 discharge summaries annotated by all teams participating in the challenge. The metrics used were recall, precision, and the F1-measure. They were calculated with exact and inexact matches, and were averaged at the level of systems and documents. Results The reference metric for this challenge, the system-level overall F1-measure, reached about 77% for exact matches, with a recall of 72% and a precision of 83%. Performance was the best with route information (F1-measure about 86%), and was good for dosage and frequency information, with F1-measures of about 82e85%. Results were not as good for durations, with F1-measures of 36e39%, and for reasons, with F1-measures of 24e27%. Conclusion The official evaluation of Textractor for the i2b2 medication extraction challenge demonstrated satisfactory performance. This system was among the 10 best performing systems in this challenge.
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paper_title: Extracting medication information from clinical text

paper_content:

The Third i2b2 Workshop on Natural Language Processing Challenges for Clinical Records focused on the identification of medications, their dosages, modes (routes) of administration, frequencies, durations, and reasons for administration in discharge summaries. This challenge is referred to as the medication challenge. For the medication challenge, i2b2 released detailed annotation guidelines along with a set of annotated discharge summaries. Twenty teams representing 23 organizations and nine countries participated in the medication challenge. The teams produced rule-based, machine learning, and hybrid systems targeted to the task. Although rule-based systems dominated the top 10, the best performing system was a hybrid. Of all medication-related fields, durations and reasons were the most difficult for all systems to detect. While medications themselves were identified with better than 0.75 F-measure by all of the top 10 systems, the best F-measure for durations and reasons were 0.525 and 0.459, respectively. State-of-the-art natural language processing systems go a long way toward extracting medication names, dosages, modes, and frequencies. However, they are limited in recognizing duration and reason fields and would benefit from future research.
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paper_title: Extracting Rx information from clinical narrative

paper_content:

Objective The authors used the i2b2 Medication Extraction Challenge to evaluate their entity extraction methods, contribute to the generation of a publicly available collection of annotated clinical notes, and start developing methods for ontology-based reasoning using structured information generated from the unstructured clinical narrative. Design Extraction of salient features of medication orders from the text of de-identified hospital discharge summaries was addressed with a knowledge-based approach using simple rules and lookup lists. The entity recognition tool, MetaMap, was combined with dose, frequency, and duration modules specifically developed for the Challenge as well as a prototype module for reason identification. Measurements Evaluation metrics and corresponding results were provided by the Challenge organizers. Results The results indicate that robust rule-based tools achieve satisfactory results in extraction of simple elements of medication orders, but more sophisticated methods are needed for identification of reasons for the orders and durations. Limitations Owing to the time constraints and nature of the Challenge, some obvious follow-on analysis has not been completed yet. Conclusions The authors plan to integrate the new modules with MetaMap to enhance its accuracy. This integration effort will provide guidance in retargeting existing tools for better processing of clinical text.
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paper_title: Textractor: a hybrid system for medications and reason for their prescription extraction from clinical text documents

paper_content:

Objective To describe a new medication information extraction systemdTextractorddeveloped for the ‘i2b2 medication extraction challenge’. The development, functionalities, and official evaluation of the system are detailed. Design Textractor is based on the Apache Unstructured Information Management Architecture (UMIA) framework, and uses methods that are a hybrid between machine learning and pattern matching. Two modules in the system are based on machine learning algorithms, while other modules use regular expressions, rules, and dictionaries, and one module embeds MetaMap Transfer. Measurements The official evaluation was based on a reference standard of 251 discharge summaries annotated by all teams participating in the challenge. The metrics used were recall, precision, and the F1-measure. They were calculated with exact and inexact matches, and were averaged at the level of systems and documents. Results The reference metric for this challenge, the system-level overall F1-measure, reached about 77% for exact matches, with a recall of 72% and a precision of 83%. Performance was the best with route information (F1-measure about 86%), and was good for dosage and frequency information, with F1-measures of about 82e85%. Results were not as good for durations, with F1-measures of 36e39%, and for reasons, with F1-measures of 24e27%. Conclusion The official evaluation of Textractor for the i2b2 medication extraction challenge demonstrated satisfactory performance. This system was among the 10 best performing systems in this challenge.
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paper_title: Extracting principal diagnosis, co-morbidity and smoking status for asthma research: evaluation of a natural language processing system

paper_content:

BackgroundThe text descriptions in electronic medical records are a rich source of information. We have developed a Health Information Text Extraction (HITEx) tool and used it to extract key findings for a research study on airways disease.MethodsThe principal diagnosis, co-morbidity and smoking status extracted by HITEx from a set of 150 discharge summaries were compared to an expert-generated gold standard.ResultsThe accuracy of HITEx was 82% for principal diagnosis, 87% for co-morbidity, and 90% for smoking status extraction, when cases labeled "Insufficient Data" by the gold standard were excluded.ConclusionWe consider the results promising, given the complexity of the discharge summaries and the extraction tasks.
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paper_title: iDASH: integrating data for analysis, anonymization, and sharing

paper_content:

iDASH (integrating data for analysis, anonymization, and sharing) is the newest National Center for Biomedical Computing funded by the NIH. It focuses on algorithms and tools for sharing data in a privacy-preserving manner. Foundational privacy technology research performed within iDASH is coupled with innovative engineering for collaborative tool development and data-sharing capabilities in a private Health Insurance Portability and Accountability Act (HIPAA)-certified cloud. Driving Biological Projects, which span different biological levels (from molecules to individuals to populations) and focus on various health conditions, help guide research and development within this Center. Furthermore, training and dissemination efforts connect the Center with its stakeholders and educate data owners and data consumers on how to share and use clinical and biological data. Through these various mechanisms, iDASH implements its goal of providing biomedical and behavioral researchers with access to data, software, and a high-performance computing environment, thus enabling them to generate and test new hypotheses.
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paper_title: GENIES : a natural-language processing system for the extraction of molecular pathways from journal articles

paper_content:

Systems that extract structured information from natural language passages have been highly successful in specialized domains. The time is opportune for developing analogous applications for molecular biology and genomics. We present a system, GENIES, that extracts and structures information about cellular pathways from the biological literature in accordance with a knowledge model that we developed earlier. We implemented GENIES by modifying an existing medical natural language processing system, MedLEE, and performed a preliminary evaluation study. Our results demonstrate the value of the underlying techniques for the purpose of acquiring valuable knowledge from biological
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paper_title: Overcoming barriers to NLP for clinical text: the role of shared tasks and the need for additional creative solutions

paper_content:

This issue of JAMIA focuses on natural language processing (NLP) techniques for clinical-text information extraction. Several articles are offshoots of the yearly ‘Informatics for Integrating Biology and the Bedside’ (i2b2) (http://www.i2b2.org) NLP shared-task challenge, introduced by Uzuner et al ( see page 552 )1 and co-sponsored by the Veteran's Administration for the last 2 years. This shared task follows long-running challenge evaluations in other fields, such as the Message Understanding Conference (MUC) for information extraction,2 TREC3 for text information retrieval, and CASP4 for protein structure prediction. Shared tasks in the clinical domain are recent and include annual i2b2 Challenges that began in 2006, a challenge for multi-label classification of radiology reports sponsored by Cincinnati Children's Hospital in 2007,5 a 2011 Cincinnati Children's Hospital challenge on suicide notes,6 and the 2011 TREC information retrieval shared task involving retrieval of clinical cases from narrative records.7 ::: ::: Although NLP research in the clinical domain has been active since the 1960s, progress in the development of NLP applications for clinical text has been slow and lags behind progress in the general NLP domain. There are several barriers to NLP development in the clinical domain, and shared tasks like the i2b2/VA Challenge address some of these barriers. Nevertheless, many barriers remain and unless the community takes a more active role in developing novel approaches for addressing the barriers, advancement and innovation will continue to be slow. ::: ::: Historically, there have been substantial barriers to NLP development in the clinical domain. These barriers are not unique to the clinical domain: they also occur in the fields of software engineering and general NLP. ::: ::: ### Lack of access to shared data ::: ::: Because of concerns regarding patient privacy and worry about revealing unfavorable institutional practices, hospitals and clinics have been extremely reluctant to allow access to clinical data for researchers from outside … ::: ::: Correspondence to Dr Wendy W Chapman, Department of Biomedical Informatics, University of California San Diego, 9500 Gilman Dr, Bldg 2 #0728, La Jolla, California, USA; wwchapman{at}ucsd.edu
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paper_title: Towards comprehensive syntactic and semantic annotations of the clinical narrative

paper_content:

Objective To create annotated clinical narratives with layers of syntactic and semantic labels to facilitate advances in clinical natural language processing (NLP). To develop NLP algorithms and open source components. ::: ::: Methods Manual annotation of a clinical narrative corpus of 127 606 tokens following the Treebank schema for syntactic information, PropBank schema for predicate-argument structures, and the Unified Medical Language System (UMLS) schema for semantic information. NLP components were developed. ::: ::: Results The final corpus consists of 13 091 sentences containing 1772 distinct predicate lemmas. Of the 766 newly created PropBank frames, 74 are verbs. There are 28 539 named entity (NE) annotations spread over 15 UMLS semantic groups, one UMLS semantic type, and the Person semantic category. The most frequent annotations belong to the UMLS semantic groups of Procedures (15.71%), Disorders (14.74%), Concepts and Ideas (15.10%), Anatomy (12.80%), Chemicals and Drugs (7.49%), and the UMLS semantic type of Sign or Symptom (12.46%). Inter-annotator agreement results: Treebank (0.926), PropBank (0.891–0.931), NE (0.697–0.750). The part-of-speech tagger, constituency parser, dependency parser, and semantic role labeler are built from the corpus and released open source. A significant limitation uncovered by this project is the need for the NLP community to develop a widely agreed-upon schema for the annotation of clinical concepts and their relations. ::: ::: Conclusions This project takes a foundational step towards bringing the field of clinical NLP up to par with NLP in the general domain. The corpus creation and NLP components provide a resource for research and application development that would have been previously impossible.
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Title: Natural Language Processing in Biomedicine: A Unified System Architecture Overview

Section 1: Introduction
Description 1: Introduce the topic of NLP in biomedicine, its importance, and provide an overview of the paper's objectives and structure.

Section 2: A General Architecture of an NLP System in Biomedicine
Description 2: Describe the general architecture of NLP systems in biomedicine, including the main components: background knowledge and integration framework.

Section 3: Background Knowledge for NLP in Biomedicine: The Unified Medical Language System (UMLS)
Description 3: Discuss the role of background knowledge, with a focus on the UMLS and its components.

Section 4: NLP Tools and Integrated Frameworks
Description 4: Explain the different approaches for building NLP tools, including rule-based and machine learning methods, and describe how these tools are integrated into frameworks like GATE and UIMA.

Section 5: System Selection
Description 5: Provide criteria for selecting representative NLP systems to review, emphasizing their historical importance and influence.

Section 6: Linguistic String Project (LSP) - Medical Language Processor (MLP)
Description 6: Detail the LSP-MLP system, its background knowledge components, and processing pipeline.

Section 7: MedLEE
Description 7: Describe the MedLEE system, its architecture, processing pipeline, and applications in biomedical NLP.

Section 8: SPRUS/SymText/MPLUS
Description 8: Summarize the SPRUS/SymText/MPLUS system, its development, and its specific applications in processing chest radiograph reports.

Section 9: MetaMap
Description 9: Discuss the MetaMap system, its architecture, use cases, and its role in mapping biomedical literature to UMLS concepts.

Section 10: HITEx
Description 10: Describe the HITEx system, its modules, and its integration with the GATE framework.

Section 11: cTAKES
Description 11: Detail the cTAKES system, its architecture, components, and applications in clinical NLP.

Section 12: Discussion and Conclusion
Description 12: Provide a comprehensive discussion on the trends, challenges, and future directions in the development of NLP systems in biomedicine, and summarize the key points of the paper.