Article

Integrating Machine Learning and SHAP for Interpretable Prediction of 28-D ay Mortality in ICU Patients: A Comprehensive Analysis of Initial Physiologic al Features

2025-11-17

Abstract excerpt

<title>Abstract</title> <p>Background Accurate, interpretable prediction of 28-day mortality in intensive care unit (ICU) patients is pivotal for timely clinical decision-making, resource optimization, and improving patient outcomes. Despite the growing application of machine learning (ML) models in mortality prediction—with reported area under the receiver operating characteristic curve (AUC) values ranging from...

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Literature Corpus work: e250d529-0771-5288-8301-099facaa9140
DOI: 10.21203/rs.3.rs-8067228/v1

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Integrating Machine Learning and SHAP for Interpretable Prediction of 28-D ay Mortality in ICU Patients: A Comprehensive Analysis of Initial Physiologic al FeaturesDOI 10.21203/rs.3.rs-8067228/v1Read

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