Personalized Gaussian Processes for Future Prediction of Alzheimer's Disease Progression
This addresses the problem of personalized disease prediction for Alzheimer's patients, representing an incremental advance in adapting existing methods.
The paper tackles predicting Alzheimer's disease progression metrics like MMSE and ADAS-Cog13 by introducing a personalized Gaussian Process model that adapts from a population-level model to individual patients, showing significant improvements in forecasting accuracy compared to traditional methods.
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.