Title:

Sequential Decision-Making Under Ambiguity with Applications to Chronic Disease Management

Abstract:

Optimization of sequential decision-making under uncertainty is important in many contexts, including chronic diseases, but ambiguity in the underlying models introduces significant challenges. In the context of chronic disease management, Markov decision processes (MDPs) have been used to optimize the delivery of medical interventions in a way that balances the immediate harms and costs with the uncertain future health benefits associated with these interventions. Unfortunately, treatment recommendations that result from MDPs can depend heavily on the model of the chronic disease, and there are often multiple plausible models due to conflicting data sources or differing opinions among medical experts. To address this problem, we introduce a new framework in which a decision-maker can consider multiple models of the MDP’s ambiguous parameters and seeks to find a strategy that maximizes the weighted performance with respect to each of these models of the MDP. We establish connections to other models in the stochastic optimization literature, derive complexity results, and establish solution methods for solving these problems. We illustrate our approach in the context of preventative treatment for cardiovascular disease, and end with a discussion of opportunities for future work by extending to other preferences towards ambiguity and other chronic diseases.

Bio:

Lauren Steimle is a Ph.D. candidate in Industrial and Operations Engineering at the University of Michigan. Her research interests are in operations research and data analytics with a focus on computational optimization and stochastic modeling for solving decision-making problems under uncertainty with applications to public health. Steimle received her M.S.E. in Industrial and Operations Engineering from the University of Michigan in 2016 and holds a B.S. in Systems Science and Engineering from Washington University in St. Louis. She is the recipient of the National Science Foundation Graduate Student Research Fellowship, a member of the third-place team in the New England Journal of Medicine’s SPRINT Data Challenge, and an Honorable Mention for the Ford Foundation Predoctoral Fellowship. She has served as President of the University of Michigan’s INFORMS Student Chapter, Student Chapter Representative on the INFORMS Subdivision Council, and Outreach Officer of the Graduate Society of Women Engineers at Michigan.