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TITLE: Sequential Disease Management of Patients with Chronic Conditions
SPEAKER: Mariel Lavieri
ABSTRACT:
Chronic disease management often involves sequential decisions that have long-term implications. Those decisions are based on high dimensional state spaces, which pose a problem for traditional modeling paradigms. In some key instances, transition probabilities might not be known, but instead are random variables that are learned as new information becomes available.
As a first step, I describe some of my ongoing research modeling screening, monitoring and treatment decisions of patients with chronic conditions. The models are motivated by diseases such as glaucoma, coronary heart disease and cancer. Key to the models developed is the incorporation of the individual patient's disease dynamics into the parameterization of the stochastic models of the disease state evolution. Model conception and validation is described, as well as the role of multidisciplinary collaborations in ensuring practical impact of my work.
