Milos Hauskrecht Title: Modeling and optimizing patient-management processes with POMDPs. Abstract: Partially observable Markov decision processes (POMDPs) offer an elegant mathematical framework for modeling and solving decision making problems with action-outcome uncertainty and imperfect state information. The focus of this talk is on POMDPs suitable for modeling and solving patient-management processes. First, I present a POMDP model we built for the management of patients with the ischemic heart disease. The model uses a hierarchical Bayesian belief network to represent the disease dynamics, and a factored cost model to represent payoffs associated with treatment choices and intermediate patient outcomes. Second, I briefly describe approximation methods that take advantage of the structure of the model and let us solve the model efficiently. Finally, I discuss a number of practical issues we encountered while building, solving and evaluating the model.