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In this paper, we introduce the use of a personalized Gaussian Process model(pGP) to predict per-patient changes in ADAS-Cog13 -- a significant predictorof Alzheimer's Disease (AD) in the cognitive domain -- using data from eachpatient's previous visits, and testing on future (held-out) data. We start bylearning a population-level model using multi-modal data from previously seenpatients 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 personalizedapproach to predict the values of ADAS-Cog13 over the future 6, 12, 18, and 24months. We compare this approach to a GP model trained only on past data of thetarget 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 largeimprovements in accurately forecasting future ADAS-Cog13 scores.

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