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 3 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Machine Learning for Real-Time Aggregated Prediction of Hospital Admission for Emergency Patients 94%
- Continuous-Time and Dynamic Suicide Attempt Risk Prediction with Neural Ordinary Differential Equations 94%
- CT-based Rapid Triage of COVID-19 Patients: Risk Prediction and Progression Estimation of ICU Admission, Mechanical Ventilation, and Death of Hospitalized Patients 93%
Similar papers in this journal
- Evaluation of Domain Generalization and Adaptation on Improving Model Robustness to Temporal Dataset Shift in Clinical Medicine 97%
- Machine learning approach to dynamic risk modeling of mortality in COVID-19: a UK Biobank study 94%
- EHR Foundation Models Improve Robustness in the Presence of Temporal Distribution Shift 94%
Similar papers in this journal
- Predicting hospital-onset COVID-19 infections using dynamic networks of patient contacts: an observational study 92%
- Real-world evaluation of AI-driven COVID-19 triage for emergency admissions: External validation & operational assessment of lab-free and high-throughput screening solutions 92%
- Multicenter Validation of a Machine Learning Algorithm for Diagnosing Pediatric Patients with Multisystem Inflammatory Syndrome and Kawasaki Disease 92%
Similar papers in this journal
- Integration of clinical characteristics, lab tests and a deep learning CT scan analysis to predict severity of hospitalized COVID-19 patients 95%
- Deep representation learning for clustering longitudinal survival data from electronic health records 94%
- Deep transfer learning for reducing health care disparities arising from biomedical data inequality 93%
Similar papers in this journal
- Benchmarking transformer-based models for medical record deidentification: A single centre, multi-specialty evaluation 93%
- A Deep Learning Approach for Culture-Free Bacterial Meningitis Diagnosis and ICU Outcome Prediction 91%
- Large-language-model-based 10-year risk prediction of cardiovascular disease: insight from the UK biobank data 91%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.