Comparison and interpretability of machine learning models to predict severity of chest injury.

ABSTRACT:

Objective

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.

Materials and methods

A level I trauma center was queried for patients presenting after injury between 2014 and 2018. Prediction modeling was performed to classify severe chest injury using a reference dataset labeled by certified registrars. Clinical documents from trauma encounters were pr

SUBMITTER: Kulshrestha S 

PROVIDER: S-EPMC7935500 | biostudies-literature | 2021 Jan

REPOSITORIES: biostudies-literature