Stochastic Modeling and Optimization to Improve Identification and Treatment of Alzheimer’s Disease
Alzheimer’s Disease (AD) is the 6th leading cause of death in the United States and affects more than 5 million people. AD can be detected at an early stage through biomarker tests including p-Tau, FDG-PET, and hippocampal. This research focuses on the formulation of a Markov Chain (MC) model to predict the AD evolution due to biomarker tests and related results from sequential patient visits to the doctor. Subsequently, a Markov Decision Process (MDP) model provides a guide to doctors to efficiently administer tests and analyze results quickly to understand the AD progression.