Online healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce the information access cost for both individuals and societies. To promote these benefits, it is desired to effectively expand the scale of high-quality yet novel relational medical entity pairs that embody rich medical knowledge in a structured form. To fulfill this goal, we introduce a generative model called Conditional Relationship Variational Autoencoder (CRVAE), which can discover meaningful and novel relational medical entity pairs without the requirement of additional external knowledge. Rather than discriminatively identifying the relationship between two given medical entities in a free-text corpus, we directly model and understand medical relationships from diversely expressed medical entity pairs. The proposed model introduces the generative modeling capacity of variational autoencoder to entity pairs, and has the ability to discover new relational medical entity pairs solely based on the existing entity pairs. Beside entity pairs, relationship-enhanced entity representations are obtained as another appealing benefit of the proposed method. Both quantitative and qualitative evaluations on real-world medical datasets demonstrate the effectiveness of the proposed method in generating relational medical entity pairs that are meaningful and novel. Increasingly, people engage in health services on the Internet BID11 . The healthcare services can provide the general public with ubiquitous access to medical knowledge and reduce the information access cost significantly. The relational medical entity pair, which consists of two medical entities with a semantic connection between them, is an intuitive representation that distills human medical reasoning processes in a structured form. The medical relationships discussed in this paper are binary ones. For example, the Disease Cause − −−− →Symptom relationship indicates a "Cause" relationship from a disease entity to a symptom entity that is caused by this disease, such as the medical entity pairs <Synovitis, Joint Pain>. For the relationship Symptom Belongto − −−−−− →Department, we may have a relational medical entity pair such as <Stiffness of a Joint, Orthopedics>.The ability to understand, reason and generalize is central to human intelligence BID27 . However , it possesses significant challenges for machines to understand and reason about the relationships between two entities BID31 . Real-world relational medical entity pairs possess certain challenging properties to deal with: First, as the medical research develops, many medical relationships among medical entities that were once neglected due to the underdeveloped medical knowledge now need to be discovered. An increasing number of relationships will be formed among a large number of medical entities. Also, various linguistic expressions can be used for the same medical entity. For example, Nose Plugged, Blocked Nose and Sinus Congestion are symptom entities that share the same meaning but expressed very differently. Moreover, one medical relationship may instantiate entity pairs with varying granularities or relationship strength. For instance, Disease Cause − −−− →Symptom may include entity pairs like <Rhinitis, Nose Plugged> as a coarse-grained entity pair, while < Acute Rhinitis, Nose Plugged>, <Chronic Rhinitis, Nose Plugged> are considered fine-grained entity pairs. As for the relationship strength, <Cold, Fatigue> has greater relationship strength than <Cold, Ear Infections> as cold rarely cause serious complications such as ear infections.To effectively expand the scale of high-quality yet novel relational medical entity pairs, relation extraction methods BID8 BID2 are proposed to examine whether or not a semantic relationship exists between two given entities given a context. Although the existing relation extraction methods BID1 BID3 BID30 BID39 BID6 BID37 achieve decent performance in identifying the relationship for given entity pairs, those methods require contexts such as sentences retrieved from a large free-text corpus, from existing domain-specific knowledge graphs BID0 , or from web tables and links BID21 . As medical relationships in the real-world are becoming more and more complex and diversely expressed, existing relation extraction methods suffer from the data sparsity problem where it is hard to obtain additional external knowledge that covers all possible entity pairs, e.g. free-text corpus where two entities co-occur in the same sentence with a relationship between them. Therefore, it is crucial and appealing for us to discover meaningful relational medical entity pairs solely based on existing medical entity pairs, without the requirement of a well-maintained context as an additional external knowledge.Furthermore, most relation extraction methods adopt a discriminative approach that learns to distinguish entity pairs of one relationship from the other BID41 BID22 , or to identify meaningful entity pairs from randomly sampled negative entity pairs with no relationships BID4 BID32 . Those methods need to iterate over the combination of all possible entity pairs and check each of them to discover new entity pairs. Such discriminative approach is tedious and labor-intensive. It is challenging yet rewarding for us to understand medical relationships intrinsically from the existing entity pairs. Specifically, in the medical domain, the diversely expressed medical entity pairs offer great advantages for us to ultimately understand medical relationships and discover high-quality relational medical entity pairs solely from existing meaningful medical entity pairs.Problem Studied: We propose a novel research problem called RElational Medical Entity-pair DiscoverY (REMEDY), which aims at modeling relational medical entity pairs solely from the existing entity pairs. Also, it aims to discover meaningful and novel entity pairs pertaining to a certain medical relationship in a generative fashion, without sophisticated feature engineering and the requirement of external knowledge such as free-text corpora.Proposed Model: A generative model named Conditional Relationship Variational Autoencoder (CRVAE) is introduced for relational medical entity pair discovery. It is unlikely to create meaningful, novel relational medical entity pairs without intrinsically understanding each medical relationship, more specifically, understanding the relationships between every two medical entities that instantiate a particular relationship. CRVAE fully explores the generative modeling capacity which roots in Bayesian inference while incorporating deep learning for powerful hands-free feature engineering. CRVAE is trained to encode each relational medical entity pair into a latent space conditioned on the relationship type. The encoding process addresses relationship-enhanced entity representations, interactions between entities as well as expressive latent variables. The latent variables are decoded to reconstruct entity pairs. Once the model is trained, we can sample directly from the distribution of latent variables and decode them into high-quality and novel relational medical entity pairs.Overall, CRVAE has three notable strengths:CRVAE models the intrinsic relations between medical entity pairs directly based on the existing meaningful relational medical entity pairs, without the requirement of additional external contexts for entity pair extraction. Existing relation extraction methods usually rely on the free-text corpus to decide whether a candidate entity pair it mentions is meaningful or not. The CRVAE only utilizes the existing entity pairs and pre-trained word vector as initial entity representations which are trained separately.CRVAE is able to generate entity pairs for a particular relationship, even if it observes existing entity pairs only for that particular relationship. Unlike most discriminative methods which harness discrepancies among different relationships to distinguish the relationship of an entity pair from the other, or from randomly constructed negative entity pairs with no relations. The CRVAE understands the intrinsic medical relation from diversely expressed medical entity pairs and discovers meaningful, novel entity pairs of a particular relationship that we specified.CRVAE generates novel entity pairs by a density-based sampling strategy in the generator. The generator samples directly from the latent space based on the density of hidden parameters. With the hands-free feature engineering by deep neural networks, the model is able to discover meaningful and novel entity pairs which does not exist in the training data.The contributions of this paper can be summarized as follows:• We study the Relational Medical Entity-pair Discovery (REMEDY) problem, which aims to expand the scale of high-quality yet novel relational medical entity pairs without maintaining large-scale context information such as the free-text corpus.• We propose a generative model named Conditional Relationship Variational Autoencoder (CRVAE) that discovers relational medical entity pairs for a given relationship, solely from the diversely expressed entity pairs without sophisticated feature engineering.• We obtain relationship-enhanced entity representations as an appealing benefit of the proposed model. To effectively expand the scale of high-quality relational medical entity pairs which store the medical knowledge, a novel generative model named Conditional Relationship Variational Autoencoder (CRVAE) is introduced for Relational Medical Entity-pair Discovery (REMEDY). The proposed model fully explores the generative modeling ability while incorporates deep learning for powerful hands-free feature engineering. Unlike traditional relation extraction tasks which require additional contexts for extraction and need negative samples for discriminative training, the proposed method learns to intrinsically understand the medical relations from diversely expressed medical entity pairs, without the requirement of external context information. Moreover, it is able to generate meaningful, novel entity pairs for a given type of medical relationship. The relationshipenhanced entity representations have the potential to improve other NLP tasks. The performance of the proposed method is evaluated on real-world medical data both quantitatively and qualitatively.
Generatively discover meaningful, novel entity pairs with a certain medical relationship by purely learning from the existing meaningful entity pairs, without the requirement of additional text corpus for discriminative extraction. Presents a variational autoencoder for generating entity pairs given a relation in a medical setting. In the medical context, this paper describes the classic problem of "knowledge base completion" from structured data only.