Accuracy of Machine Learning Models to Predict In-hospital Cardiac Arrest: A Systematic Review.

Journal: Clinical nurse specialist CNS
Published Date: May 13, 2025

Abstract

PURPOSE/AIMS: Despite advances in healthcare, the incidence of in-hospital cardiac arrest (IHCA) has continued to rise for the past decade. Identifying those patients at risk has proven challenging. Our objective was to conduct a systematic review of the literature to compare the IHCA predictive performance of machine learning (ML) models with the Modified Early Warning Score (MEWS).

Authors

  • Laura M Moffat
    Author Affiliations: Clinical Assistant Professor (Dr Moffat) and Assistant Professor (Dr Xu), School of Nursing, Purdue University, West Lafayette, Indiana.
  • Dongjuan Xu
    Department of Neurology, Dongyang People's Hospital, Affiliated to Wenzhou Medical University, Dongyang, China.

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