Extracting Clinical Relations in Electronic Health Records Using Enriched Parse Trees

Abstract

Integrating semantic features into parse trees is an active research topic in open-domain natural language processing (NLP). We study six different parse tree structures enriched with various semantic features for determining entity relations in clinical notes using a tree kernel-based relation extraction system. We used the relation extraction task definition and the dataset from the popular 2010 i2b2/VA challenge for our evaluation. We found that the parse tree structure enriched with entity type suffixes resulted in the highest F1 score of 0.7725 and was the fastest. In terms of reducing the number of feature vectors in trained models, the entity type feature was most effective among the semantic features while adding semantic feature node was better than adding feature suffixes to the labels. Our study demonstrates that parse tree enhancements with semantic features are effective for clinical relation extraction.