Paper: RelAgent: Entity Detection and Normalization for Diseases in Clinical Records: a Linguistically Driven Approach

ACL ID S14-2083
Title RelAgent: Entity Detection and Normalization for Diseases in Clinical Records: a Linguistically Driven Approach
Venue Joint Conference on Lexical and Computational Semantics
Session
Year 2014
Authors

We refined the performance of Co- coa/Peaberry, a linguistically moti- vated system, on extracting disease en- tities from clinical notes in the train- ing and development sets for Task 7. Entities were identified in noun chunks by use of dictionaries, and events (?The left atrium is dilated?) through our own parser and predicate-argument struc- tures. We also developed a mod- ule to map the extracted entities to the SNOMED subset of UMLS. The module is based on direct matching against UMLS entries through regu- lar expressions derived from a small set of morphological transformations, along with priority rules when multi- ple UMLS entries were matched. The performance on training and develop- ment sets was 81.0% and 83.3% respec- tively (Task A), and the UMLS match- ing scores were respec...