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

摘要: In the electronic health record, using clinical notes to identify entities such as disorders and their temporality (eg the order of an event relative to a time index) can inform many important analyses. However, creating training data for clinical entity tasks is time consuming and sharing labeled data is challenging due to privacy concerns. The information needs of the COVID-19 pandemic highlight the need for agile methods of training machine learning models for clinical notes. We present Trove, a framework for weakly supervised entity …