TY - GEN
T1 - Implicit entity recognition in clinical documents
AU - Perera, Sujan
AU - Mendes, Pablo
AU - Sheth, Amit
AU - Thirunarayan, Krishnaprasad
AU - Alex, Adarsh
AU - Heid, Christopher
AU - Mott, Greg
PY - 2015
Y1 - 2015
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=84966374220&partnerID=8YFLogxK
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U2 - 10.18653/v1/s15-1028
DO - 10.18653/v1/s15-1028
M3 - Conference contribution
AN - SCOPUS:84966374220
T3 - Proceedings of the 4th Joint Conference on Lexical and Computational Semantics, *SEM 2015
SP - 228
EP - 238
BT - Proceedings of the 4th Joint Conference on Lexical and Computational Semantics, *SEM 2015
PB - Association for Computational Linguistics (ACL)
T2 - 4th Joint Conference on Lexical and Computational Semantics, *SEM 2015
Y2 - 4 June 2015 through 5 June 2015
ER -