Ontology-driven weak supervision for clinical entity classification in electronic health records

• DOI: 10.1038/s41467-021-22328-4
• Publication Date: 2021-04-01T10:04:19Z
• Authors: Jason A. Fries; Ethan Steinberg; Saelig Khattar; Scott L. Fleming; Jose Posada; Alison Callahan; Nigam H. Shah
• Keywords: Health records; Benchmark (surveying); Electronic health record; Medical record

Abstract In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (e.g. order of an event relative a time index) can inform many important analyses. However, creating training data for entity tasks is consuming sharing labeled challenging due privacy concerns. The information needs COVID-19 pandemic highlight need agile methods machine learning models notes. We present Trove, framework weakly supervised classification medical ontologies expert-generated rules. Our approach, unlike hand-labeled notes, easy share modify, while offering performance comparable from manually data. this work, we validate our on six benchmark demonstrate Trove’s ability analyze records patients visiting emergency department at Stanford Health Care presenting symptoms risk factors.