ArticleScientific reports2025
Using machine learning for early prediction of in-hospital mortality during ICU admission in liver cancer patients.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.