Isaac Tosin Adisa

Evaluating Uncertainty Quantification in Clinical Machine Learning: Calibration, Robustness, and Decision Utility under Distribution Shift

A rigorous empirical framework is presented for comparing three uncertainty quantification approaches on two clinical prediction tasks, in-hospital mortality and 30-day readmission, using 74,829 ICU admissions from the MIMIC-IV database to support a more demanding evaluation standard for UQ in clinical machine learning.