Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13).

In this paper, we introduce the use of a personalized Gaussian Process pGP model to predict per-patient changes in ADAS-Cog13-a significant predictor of Alzheimer's Disease (AD) in the cognitive domain - using data from each patient's previous visits, and testing on future (held-out) data. We start by learning a population-level model using multi- modal data from previously seen patients using a base Gaussian Process (GP) regression. The pGP is then formed by adapting the base GP sequentially over time to a new (target) patient using domain adaptive GPs [1].