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

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

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-maki...

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Bibliographic Details
Main Authors: Zhengqiu Yu, Lexin Fang, Yueping Ding
Format: Article
Language:English
Published: BMC 2025-05-01
Series:European Journal of Medical Research
Subjects:
Mortality prediction
Immunocompromised
Intensive care unit
Machine learning
MIMIC-IV
SHAP value
Online Access:https://doi.org/10.1186/s40001-025-02622-3
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https://doi.org/10.1186/s40001-025-02622-3

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