Anticipating Meals with Behavioral Profiles: Towards Stochastic Model Predictive Control of T1DM*

The delay associated with subcutaneous glucose sensing and insulin infusion actuation significantly complicates the design of control algorithms for regulating blood glucose in patients with Type 1 Diabetes Mellitus (T1DM). Model predictive control (MPC) is one strategy for mitigating delay, where optimal insulin infusions can be given in anticipation of future meal disturbances. Unfortunately, exact prior knowledge of meals can only be assured in a clinical environment, and uncertainty about when and if meals will arrive could lead to catastrophic outcomes. In this paper we develop an MPC-like control law that can anticipate meals given a probabilistic description of the patient's eating behavior in the form of a random meal (behavioral) profile. Preclinical in silico trials using the oral glucose meal model of Dalla Man et al. show that the control strategy provides a convenient means to account for uncertain prior knowledge of meals without compromising patient safety, even in the event that anticipated meals are skipped.