  In this paper, we introduce the use of a personalized Gaussian Process model
(pGP) 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 personalized GP
(pGP) is formed by adapting the base GP sequentially over time to a new
(target) patient using domain adaptive GPs. We extend this personalized
approach to predict the values of ADAS-Cog13 over the future 6, 12, 18, and 24
months. We compare this approach to a GP model trained only on past data of the
target patients (tGP), as well as to a new approach that combines pGP with tGP.
We find that the new approach, combining pGP with tGP, leads to large
improvements in accurately forecasting future ADAS-Cog13 scores.
