Implicit Entity Recognition in Clinical Documents

TitleImplicit Entity Recognition in Clinical Documents
Publication TypeConference Proceedings
Year of Publication2015
AuthorsSujan Perera, Pablo Mendes, Amit Sheth, Krishnaprasad Thirunarayan, Adarsh Alex, Christopher Heid, Greg Mott
Conference Name4th Joint Conference on Lexical and Computational Semantics (*SEM) 2015
Date Published06/2015
PublisherAssociation for Computational Linguistics
Conference LocationDenver, CO
ISBN Number978-1-941643-39-6
KeywordsClinical entity recognition, Implicit information extraction, Negation detection, semantic similarity

With the increasing automation of health care information processing, it has become crucial to extract meaningful information from textual notes in electronic medical records. One of the key challenges is to extract and normalize entity mentions. State-of-the-art approaches have focused on the recognition of entities that are explicitly mentioned in a sentence. However, clinical documents often contain phrases that indicate the entities but do not contain their names. We term those implicit entity mentions and introduce the problem of implicit entity recognition (IER) in clinical documents. We propose a solution to IER that leverages entity definitions from a knowledge base to create entity models, projects sentences to the entity models and identifies implicit entity mentions by evaluating semantic similarity between sentences and entity models. The evaluation with 857 sentences selected for 8 different entities shows that our algorithm outperforms the most closely related unsupervised solution. The similarity value calculated by our algorithm proved to be an effective feature in a supervised learning setting, helping it to improve over the baselines, and achieving F1 scores of .81 and .73 for different classes of implicit mentions. Our gold standard annotations are made available to encourage further research in the area of IER.