Interpretable machine learning for prognostic prediction in critically ill patients with coronary artery disease: a multicenter study

Abstract

The random forest model developed in this study exhibited robust predictive performance and generalization capability in evaluating short-term and long-term mortality risks among critically ill patients with CAD. As a promising predictive tool, it offers data-driven decision support for clinicians to conduct early identification of high-risk patients and perform risk stratification, while its ultimate clinical utility remains to be further validated by prospective studies.

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