Construction and validation of prognostic model for ICU mortality in cardiac arrest patients: an interpretable machine learning modeling approach

Construction and validation of prognostic model for ICU mortality in cardiac arrest patients: an interpretable machine learning modeling approach

Abstract Background The incidence and mortality of cardiac arrest (CA) is high. We developed interpretable machine learning models for early prediction of ICU mortality risk in patients diagnosed with CA. Methods Data from the Medical Information Mart for Intensive Care (MIMIC-IV, version 2.2) was r...

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Bibliographic Details
Main Authors: Yong Li, Ying Liu, Qing Zhang, Hongwei Zhu, Chengli Wen, Xian Jiang
Format: Article
Language:English
Published: BMC 2025-04-01
Series:European Journal of Medical Research
Subjects:
Cardiac arrest
Random forest
ICU mortality risk
Shapley additive explanations
Online Access:https://doi.org/10.1186/s40001-025-02588-2
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