Development and validation of an interpretable machine learning model for predicting in-hospital mortality among critically ill patients with liver cirrhosis
5 affiliations
- 1Department of Infectious Diseases, Yiling Hospital of Yichang, Affiliated Yiling Hospital of China Three Gorges University
- 2Department of Emergency and Critical Care Medicine, Yiling Hospital of Yichang, Affiliated Yiling Hospital of China Three Gorges University
- 3Department of Emergency, Guangxi University Key Laboratory of Emergency Medicine, The First Affiliated Hospital of Guangxi Medical University
- 4Department of Hepatobiliary and Pancreatic Surgery, The Second Affiliated Hospital of Kunming Medical University
- 5Department of Infectious Diseases, The Central Hospital of Xiaogan, Xiaogan Hospital Affiliated to Wuhan University of Science and Technology
Abstract
This study aimed to develop and validate an interpretable machine learning model for early prediction of in-hospital mortality in critically ill patients with liver cirrhosis to optimize risk stratification and individualized clinical intervention. A total of 5358 cirrhotic ICU patients were retrospectively selected from the MIMIC-IV database and randomly divided into training and internal validation cohorts at a 7:3 ratio. Twelve machine learning algorithms were constructed and compared with the conventional SAPS II score. Model performance was evaluated via AUC-ROC, and SHAP analysis was used to improve model transparency and clinical interpretability. The CatBoost model showed the best predictive performance with an AUC of 0.79, significantly superior to SAPS II (AUC = 0.75, p < 0.001). SHAP analysis identified minimum lactate, body temperature, blood urea nitrogen and white blood cell count as the top four influential predictors. Using routine clinical indicators collected within 24 h of ICU admission, this interpretable model showed better discrimination than SAPS II for predicting in-hospital mortality in critically ill patients with cirrhosis. It may assist early risk stratification, but further external validation is needed before clinical application.
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BibTeX
@article{guo2026development,
title = {{Development and validation of an interpretable machine learning model for predicting in-hospital mortality among critically ill patients with liver cirrhosis}},
author = {Fengjie Guo and Shijie Zhong and Yang Tan and Yong Yang and Fangchao Chen and Qiang Hu and Yuchen Zhang and Hong Zhu and Song Ren},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-62650-9},
url = {https://doi.org/10.1038/s41598-026-62650-9},
note = {CC BY},
}
RIS
TY - JOUR
TI - Development and validation of an interpretable machine learning model for predicting in-hospital mortality among critically ill patients with liver cirrhosis
AU - Fengjie Guo
AU - Shijie Zhong
AU - Yang Tan
AU - Yong Yang
AU - Fangchao Chen
AU - Qiang Hu
AU - Yuchen Zhang
AU - Hong Zhu
AU - Song Ren
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-62650-9
UR - https://doi.org/10.1038/s41598-026-62650-9
AB - This study aimed to develop and validate an interpretable machine learning model for early prediction of in-hospital mortality in critically ill patients with liver cirrhosis to optimize risk stratification and individualized clinical intervention. A total of 5358 cirrhotic ICU patients were retrospectively selected from the MIMIC-IV database and randomly divided into training and internal validation cohorts at a 7:3 ratio. Twelve machine learning algorithms were constructed and compared with the conventional SAPS II score. Model performance was evaluated via AUC-ROC, and SHAP analysis was used to improve model transparency and clinical interpretability. The CatBoost model showed the best predictive performance with an AUC of 0.79, significantly superior to SAPS II (AUC = 0.75, p < 0.001). SHAP analysis identified minimum lactate, body temperature, blood urea nitrogen and white blood cell count as the top four influential predictors. Using routine clinical indicators collected within 24 h of ICU admission, this interpretable model showed better discrimination than SAPS II for predicting in-hospital mortality in critically ill patients with cirrhosis. It may assist early risk stratification, but further external validation is needed before clinical application.
SN - 2045-2322
ER -