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

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.