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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.

Symposium Participant

Portrait of Camarena, Raquel

Raquel Camarena

Project Details

Symposium Date: Spring 2018

Research Theme: Health

Presentation Type: MORE

Faculty Mentors

  • Giulia Pedrielli