Prospective validation and real-time implementation of an automated machine learning postoperative mortality prediction model.

ABSTRACT:

Background

Machine learning prediction models require prospective validation to ensure implementation fidelity and feasibility. Our primary objective was to prospectively validate a previously reported postoperative mortality prediction model in inpatients undergoing surgery. Our secondary objective was to evaluate feasibility of a pilot clinical decision support tool.

Methods

We prospectively validated and implemented a random forest machine learning model trained to predict in-hospital mortality using data from a single academic medical centre. A reduced 32-feature model was implemented into the electronic health record (EHR) using a real-time data mart at the same institution. To assess model performance, the area under the receiver operating characteristic curve (AUROC),

SUBMITTER: Wingert T 

PROVIDER: S-EPMC12927408 | biostudies-literature | 2026 Jan

REPOSITORIES: biostudies-literature