Data-Driven Anticipation of Meal Intakes for Automated Insulin Delivery Systems with Model Predictive Control Technology

Controlling postprandial glucose excursions in type 1 diabetes remains a challenge for most automated insulin delivery systems due to the slow absorption of insulin analogs. This limitation prevents controllers from responding aggressively enough to the rapid glucose spikes following meals to avoid the risk of insulin over-delivery and late hypoglycemia. In this paper, we propose an algorithmic solution to this problem based on the concept of meal anticipation. We propose a data-driven method to predict meal disturbances and anticipate insulin delivery with Model Predictive Control (MPC) frameworks. In contrast to previous works, this strategy does not add significant computational complexity to the original MPC algorithm and, therefore, it is better suited for implementation in wearable devices. Simulations using the University of Virginia Type 1 Diabetes Simulator and its clinically validated MPC algorithm demonstrate an 8% increase in time-in-range along with reductions in hypoglycemia events.