Utsumi, Y., Rudovic, O., Peterson, K., Guerrero, R., Picard, R. "Personalized Gaussian Processes for Forecasting of Alzheimer's Disease Assessment Scale-Cognition Sub-Scale (ADAS-Cog13)." The 40th International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC). May 2018.

Abstract

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 multimodal 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]. 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 this new approach (pGP+tGP) leads to significant improvements in accurately forecasting future ADAS-Cog13 scores.