Relation Detection to Identify Stroke Assertions from Clinical Notes Using Natural Language Processing

Abstract

According to the World Stroke Organization, 12.2 million people world-wide will have their first stroke this year almost half of which will die as a result. Natural Language Processing (NLP) may improve stroke phenotyping; however, existing rule-based classifiers are rigid, resulting in inadequate performance. We report findings from a pilot study using NLP to improve relation detection for stroke assertion detection to support research studies and healthcare operations.