Decomposing the evaluation of probabilistic prediction of future cognitive decline in aging

Brisbane Convention & Exhibition Centre 

Current efforts on the prediction of cognitive decline from demographic, genetic, and brain imaging features primarily focus on: (1) predicting future diagnoses1, (2) generating point estimates (e.g., expected values)2, and (3) using fixed time windows3,4. A probabilistic approach predicting future cognitive decline trajectories offers significant advantages by capturing the uncertainty of predictions, accommodating arbitrary time intervals, and enabling personalized trajectories that account for individual variability in disease progression. Despite benefits, evaluating probabilistic forecasts poses greater challenges than point estimates due to the need for robust calibration and discrimination assessments, ensuring that predicted probabilities can be used for adjustable decision thresholds with confidence guarantees that meet requirements for future translation of such models into actionable insights5. In this work, we introduce probabilistic forecasting of future Clinical Dementia Rating Sum of Boxes (CDR-SOB)6, often used as primary outcome measures in clinical trials of Alzheimer's disease (AD), and the multidimensional evaluation of performance.