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. 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 mortality based on clinical characteristics, laboratory results, and severity scores. Key features influencing the prediction included APSIII, SAPSII, LODS, OASIS, and vital signs such as heart rate, temperature, and oxygen saturation. Feature importance analysis revealed that clinical severity scores played a major role in predicting mortality.
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