Faculty, Staff and Student Publications
Language
English
Publication Date
11-18-2025
Journal
Scientific Reports
DOI
10.1038/s41598-025-24369-x
PMID
41254126
PMCID
PMC12627085
PubMedCentral® Posted Date
11-18-2025
PubMedCentral® Full Text Version
Post-print
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 mortality based on clinical characteristics, laboratory results, and severity scores. Performance was evaluated using accuracy, AUROC, AUPRC, F1 score, and Kaplan-Meier curves. A total of 1,554 patients were included. The random forest model demonstrated the best performance, with an AUROC of 0.911 (95% CI: 0.855-0.956) and an AUPRC of 0.823 (95% CI: 0.718-0.905) in the internal test set, and an AUROC of 0.857 (95% CI: 0.826-0.889) and an accuracy of 0.828 (95% CI: 0.802-0.857) in the external validation set. Kaplan-Meier curves showed that all four models effectively stratified high-risk and low-risk groups. 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. This study demonstrates the potential of machine learning algorithms, particularly random forest, in predicting in-hospital mortality for critically ill liver cancer patients in the ICU. The identified clinical features provide valuable insights for clinicians in assessing patient risk and making timely interventions.
Keywords
Humans, Machine Learning, Female, Male, Middle Aged, Intensive Care Units, Hospital Mortality, Liver Neoplasms, Aged, Retrospective Studies, Prognosis, Algorithms, Liver cancer, In-hospital mortality, Machine learning, Intensive care unit
Published Open-Access
yes
Recommended Citation
Zheng, Zhuo; Xia, Jinhong; Luo, Jiawei; et al., "Using Machine Learning for Early Prediction of In-Hospital Mortality During ICU Admission in Liver Cancer Patients" (2025). Faculty, Staff and Student Publications. 808.
https://digitalcommons.library.tmc.edu/uthshis_docs/808