Temporal Feature Engineering and Ensemble Learning for Predicting 28-Day Mortality in ICU Patients with Alcoholic Cirrhosis
Sanjaya, J.; Haghi, M.; Kudrot, N.; Pathak, S.; Chandramouli, S. V.; Alaei, K.; Pishgar, M.
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Background: Predicting 28-day mortality in ICU patients with alcoholic cirrhosis is challenging because clinical deterioration is dynamic and heterogeneous. Methods: Using MIMIC-IV (v3.1), this study included 1,907 patients (training n = 1,334; validation n = 573), engineering 208 temporal and static predictors from 64 base variables and reducing them to 40 through multi-stage selection. Seven classifiers and a weighted gradient-boosting ensemble (XGBoost, CatBoost, LightGBM) were compared with Optuna tuning. Results: The ensemble achieved the highest internal validation AUC (0.9276; 95% CI: 0.9011-0.9507) and lowest Brier score (0.0870), with strong discrimination on eICU-CRD (AUC 0.9347) and related MIMIC-III (AUC 0.9071). Ablation indicated that temporal features, especially deltas, were major contributors ({triangleup}AUC {approx} 0.17 when removed). SHAP highlighted APS III score, anion gap, oxygen saturation (delta), lactate, and INR as leading predictors. Conclusions: The framework supports interpretable, trajectory-informed risk stratification in critically ill cirrhotic patients; prospective validation is needed before clinical use.
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