Data-Driven Modeling and Predictive Control of Chronic Diseases: An Ulcerative Colitis Application
The clinical management of chronic disease is naturally structured as a feedback control problem. Physicians observe the disease state at clinic visits and make decisions based on the patient's history and current state. We propose a framework that models the dynamics of chronic disease in response to therapy as an event-based controlled stochastic system, in which treatment decisions are made at discrete, irregularly spaced events while the disease evolves between events as a continuous-time Markov chain (CTMC) with state and input dependent transition rates. We demonstrate this framework in the setting of ulcerative colitis, where we identify the model from real-world clinical data in the electronic health record (EHR) of more than 3,000 patients with ulcerative colitis. Using the learned model, we formulate therapy selection as a model predictive control problem (MPC) that maximizes the expected time spent in inactive disease states, and we evaluate the resulting policy. In closed-loop simulation on 10 patients, the MPC achieves a cost at or below the observed clinical course in the majority of its realizations for 4 of the 10 patients, and its outcomes in all cases are comparable to observed care and within clinical guidelines.