Uncertainty-Aware Longitudinal Forecasting of Alzheimer's Disease Progression Using Deep Learning

A new probabilistic framework for forecasting Alzheimer's disease progression has been proposed, incorporating a Temporal Fusion Transformer with a CORAL ordinal output layer and an autoregressive Mixture Density Network to generate five-year probabilistic trajectories for various clinical metrics. This model outperforms existing linear, recurrent, and transformer baselines, particularly in distinguishing between mild cognitive impairment and dementia, achieving approximately 90% credible interval coverage while effectively separating aleatoric from epistemic uncertainty. This advancement is significant for practitioners as it enhances the reliability of long-term predictions in clinical settings, providing deeper insights into the dynamics of disease progression and uncertainty management.