A longitudinal pattern based approach to predict Alzheimer's disease via

 
   Lead Investigator:    Ying Lin
   Institution      :    University of Washington
   E-Mail           :    liny90@uw.edu
   Proposal ID      :    907



   Proposal Description:
   Identifying the individuals who will progress to AD in an early stage is          critical for initializing the preventive care and treatment of AD. Predicting     the individuals' disease progression from the interactive, longitudinal, and      multivariate measurements, e.g. clinical measurements, neuropsychological         tests, and health history, is challenging. We aim to develop a rule-based         method to 1) discover a set of risk predictive patterns to capture both           progression trajectory in individual measurement and interactions between         multiple measurements 2) fusing these patterns to assess the individual's risk    of disease onset. The overall goal is to use the multivariate longitudinal        measurements for accurately predicting AD.