Peterson, K., Rudovic, O., Guerrero, R., Picard, R. "Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression." ML4H: Machine Learning for Health. NIPS'W (2017)

Abstract

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.