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

20260 citationsJournal Articlehybrid Open Access

Authors

Theodora Wingert · University of California, Los Angeles

Tiffany Williams · University of California, Los Angeles

Briana Syed · John Sealy Hospital

Brian Hill · Agile RF (United States)

Tristan Grogan · University of California, Los Angeles

Andrew Young · University of California, Los Angeles

Zarah Antongiorgi

Abstract

This prospective validation and EHR implementation of a previously published random forest machine learning model predicting postoperative in-hospital mortality demonstrated acceptable real-world performance of the implemented model and feasibility of integrating such a system into clinical practice.

Topics & Keywords

Sepsis Diagnosis and Treatment Cardiac, Anesthesia and Surgical Outcomes Machine Learning in Healthcare

UN Sustainable Development Goals

Good health and well-being

Publication Details

Published in: British Journal of Anaesthesia

DOI: 10.1016/j.bja.2025.11.042

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