  In this paper, we introduce the use of a personalized Gaussian Process model
(pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE,
ADAS-Cog13, CDRSB and CS) based on each patient's previous visits. We start by
learning a population-level model using multi-modal data from previously seen
patients using the base Gaussian Process (GP) regression. Then, this model is
adapted sequentially over time to a new patient using domain adaptive GPs to
form the patient's pGP. We show that this new approach, together with an
auto-regressive formulation, leads to significant improvements in forecasting
future clinical status and cognitive scores for target patients when compared
to modeling the population with traditional GPs.
