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We introduce a novel personalized Gaussian Process Experts (pGPE) model forpredicting per-subject ADAS-Cog13 cognitive scores -- a significant predictorof Alzheimer's Disease (AD) in the cognitive domain -- over the future 6, 12,18, and 24 months. We start by training a population-level model usingmulti-modal data from previously seen subjects using a base Gaussian Process(GP) regression. Then, we personalize this model by adapting the base GPsequentially over time to a new (target) subject using domain adaptive GPs, andalso by training subject-specific GP. While we show that these models achieveimproved performance when selectively applied to the forecasting task (oneperforms better than the other on different subjects/visits), the averageperformance per model is suboptimal. To this end, we used the notion of metalearning in the proposed pGPE to design a regression-based weighting of theseexpert models, where the expert weights are optimized for each subject andhis/her future visit. The results on a cohort of subjects from the ADNI datasetshow that this newly introduced personalized weighting of the expert modelsleads to large improvements in accurately forecasting future ADAS-Cog13 scoresand their fine-grained changes associated with the AD progression. Thisapproach has potential to help identify at-risk patients early and improve theconstruction of clinical trials for AD.

Meta-Weighted Gaussian Process Experts for Personalized Forecasting of AD Cognitive Changes