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Interpretable machine learning for early in-hospital mortality prediction in intensive care unit patients with acute
Jianshan Shi1, Yanfen Li1, Wenxuan Li2
1School of Intelligent Medicine and Technology (Big Data Research Center), Hainan Engineering Research Center for Health Big Data, The First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 571199, China; Key Laboratory of Emergency and Trauma of Ministry of Education, Department of Interventional Vascular Surgery, Department of Infectious Diseases, The First Affiliated Hospital of Hainan Medical University, Hainan Medical University, Haikou, 570102, China.
Background:
Early mortality risk stratification remains challenging in intensive care unit (ICU) patients with acute upper gastrointestinal bleeding (AUGIB). We developed a parsimonious, interpretable ensemble model and evaluated its performance across independent ICU cohorts.
Methods:
MIMIC-IV was used for model development and internal validation (n=3,728), and eICU-CRD (n=7,529) and a Hainan cohort (n=200) were used for external validation. Adults with ICU stays of at least 24 hours were included. Subsequent all-cause in-hospital mortality was predicted from routinely available data collected during the first 24 hours after ICU admission. Within MIMIC-IV, predictors were prioritized according to multi-algorithm ranking and clinical feasibility, and a 17-feature subset was selected using a performance-parsimony criterion supported by knee-point analysis. Nine machine-learning algorithms were evaluated, and four complementary learners were combined using equal-weight soft voting based on cross-validated performance and prediction complementarity. Model evaluation included AUROC, PR-AUC, Brier score, calibration, and decision-curve analysis. Ensemble-level SHAP and pathway analyses supported model interpretation.
Results:
AUGIB Soft-Voting Ensemble (AUGIB-SVE) achieved AUROCs of 0.847, 0.837, and 0.848 in MIMIC-IV, eICU, and Hainan validation, respectively. It also achieved higher AUROCs than AIMS65, pre-endoscopic Rockall, SOFA, and SAPS II in the cohorts in which these scores could be reconstructed. Decision-curve analysis showed positive net benefit across clinically relevant threshold ranges. SHAP and pathway-support analyses linked influential predictors to four clinically coherent biological axes: hemorrhage/perfusion, coagulation dysfunction, inflammation/hypoxia, and multiorgan dysfunction. AUGIB-SVE was deployed as an online risk-prediction tool.
Conclusions:
AUGIB-SVE provides a parsimonious and interpretable framework for early in-hospital mortality risk stratification in ICU patients with AUGIB. It showed consistent discrimination across independent cohorts, favorable clinical utility, and higher discrimination than reconstructable conventional scores.
Trial Registration:
Not applicable.