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.
Peterson et al. REF proposed to use a similar model for diagnosis of Alzheimer's disease, where a population-level GP is adapted to a new patient using domain GPs individually.
Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression
ArXiv
ArXiv
Mathematics Computer Science Biology