Reconstructing and forecasting disease trajectories of patients with Alzheimer's disease using routine data in resource-constrained settings

This research aims to reconstruct and forecast Alzheimer's disease trajectories using routine data in resource-constrained settings. It proposes a unified framework for bidirectional prediction of cognitive scores from irregular visits, enabling interpolation and extrapolation, and providing calibrated uncertainty estimates.