Explainable machine learning model for prediction of 28-day all-cause mortality in immunocompromised patients in the intensive care unit: a retrospective cohort study based on MIMIC-IV database.

Journal: European journal of medical research
PMID: 40319284

Abstract

OBJECTIVES: This study aimed to develop and validate an explainable machine learning (ML) model to predict 28-day all-cause mortality in immunocompromised patients admitted to the intensive care unit (ICU). Accurate and interpretable mortality prediction is crucial for clinical decision-making and optimal allocation of critical care resources for this vulnerable patient population.

Authors

  • Zhengqiu Yu
    School of Medicine, Xiamen University, 422 South Siming Road, Xiamen, 361005, Fujian, China.
  • Lexin Fang
    Department of Critical Care Medicine, The Second Affiliated Hospital of Zhejiang Chinese Medical University, 318 Chaowang Road, Hangzhou, 31000, Zhejiang, China.
  • Yueping Ding
    Department of Critical Care Medicine, The Second Affiliated Hospital of Zhejiang Chinese Medical University, 318 Chaowang Road, Hangzhou, 31000, Zhejiang, China. Dingyp0424@zcmu.edu.cn.

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