Comparison and interpretability of machine learning models to predict severity of chest injury.
Trauma quality improvement programs and registries improve care and outcomes for injured patients. Designated trauma centers calculate injury scores using dedicated trauma registrars; however, many injuries arrive at nontrauma centers, leaving a substantial amount of data uncaptured. We propose automated methods to identify severe chest injury using machine learning (ML) and natural language processing (NLP) methods from the electronic health record (EHR) for quality reporting.
Author(s): Kulshrestha, Sujay, Dligach, Dmitriy, Joyce, Cara, Gonzalez, Richard, O'Rourke, Ann P, Glazer, Joshua M, Stey, Anne, Kruser, Jacqueline M, Churpek, Matthew M, Afshar, Majid
DOI: 10.1093/jamiaopen/ooab015