Using Stochastic Decision Making Models to Improve Alzheimer's Disease 
   Lead Investigator:    Saeideh Mirghorbani
   Institution      :    Binghamton University
   E-Mail           :    smirghor@binghamton.edu
   Proposal ID      :    1438



   Proposal Description:
   We want to propose a mathematical model (using stochastic decision-making models such as Markov decision process or partially observable Markov decision process) that determines optimal screening plans for Alzheimer's Disease in patients who are more susceptible to have these disease such as patients with diabetes or high blood pressure.  To form the model, we need to consider different stages of the Alzheimer's Disease progression and estimate the rates of transition from each stage to the other stages. This dataset will be used in this part of our study. In our model, we aim to maximize patient quality-adjusted life years (QALY) while minimizing related costs.