Using machine learning for early prediction of in-hospital mortality during ICU admission in liver cancer patients.

ABSTRACT: Liver cancer has a high incidence and mortality rate globally, particularly in patients requiring intensive care unit (ICU) admission. Early prediction of in-hospital mortality for these patients is crucial, yet lacking reliable tools. This study aims to develop and evaluate machine learning (ML) models for predicting in-hospital mortality in critically ill liver cancer patients admitted to the ICU. This retrospective study used data from the MIMIC-III and MIMIC-IV databases, including 862 patients from MIMIC-III (training cohort) and 692 patients from MIMIC-IV (validation cohort). The study focused on patients diagnosed with liver cancer, identified by specific ICD codes. Four ML algorithms, namely logistic regression, random forest, XGBoost, and LightGBM, were used to predict in-hospital

SUBMITTER: Zheng Z 

PROVIDER: S-EPMC12627085 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature