Improving machine learning and deep learning models for 30-day ICU readmission prediction using Ensemble Bayesian Model Averaging
Koumantakis, E.; Remoundou, K.; Fava, C.; Roussaki, I.; Visconti, A.; Berchialla, P.
Show abstract
Intensive Care Unit (ICU) readmissions are associated with adverse clinical outcomes and increased healthcare costs. Although existing models for predicting 30-day ICU readmission show high predictive performance, they fail to account for model uncertainty, potentially resulting in overconfident and unreliable decision-making. We propose a novel Ensemble Bayesian Model Averaging (EBMA)-based framework which balances predictive discrimination with uncertainty by penalizing models that are confident but incorrect. It achieved excellent calibration (Brier score = 0.051), while maintaining discriminatory performance comparable to or exceeding that of the best individual models (AUROC > 0.716). These findings suggest that our EBMA-based framework provides a more robust and clinically reliable approach for ICU readmission prediction and decision support.
Matching journals
The top 6 journals account for 50% of the predicted probability mass.