2017/11/30 by Kelly Peterson, Peterson, Kelly, Ognjen Rudovic +5
Biochemistry, Genetics and Molecular Biology · Computer Science · Environmental Science · #FOS: Biological sciences #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Health, Environment, Cognitive Aging #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine Learning in Healthcare #Metabolomics and Mass Spectrometry Studies #Quantitative Methods (q-bio.QM)
paper · pdf · doi:10.48550/arxiv.1712.00181
openalex publication_date 2017/11/30 · openalex created_date 2022/09/30 · openalex updated_date 2026/07/28
In this paper, we introduce the use of a personalized Gaussian Process model\n(pGP) to predict the key metrics of Alzheimer's Disease progression (MMSE,\nADAS-Cog13, CDRSB and CS) based on each patient's previous visits. We start by\nlearning a population-level model using multi-modal data from previously seen\npatients using the base Gaussian Process (GP) regression. Then, this model is\nadapted sequentially over time to a new patient using domain adaptive GPs to\nform the patient's pGP. We show that this new approach, together with an\nauto-regressive formulation, leads to significant improvements in forecasting\nfuture clinical status and cognitive scores for target patients when compared\nto modeling the population with traditional GPs.\n