CONTROLLING BLOOD GLUCOSE LEVELS IN PATIENTS WITH TYPE 1 DIABETES USING FITTED Q-ITERATIONS AND FUNCTIONAL FEATURES

Type 1 Diabetes is characterized by the lack of insulin-producing beta cells in the pancreas. The artificial pancreas promises to alleviate the burdens of self-management. While the physical components of the system – the continuous glucose monitor and insulin pump – have experienced rapid advances, a technological bottleneck remains in the control algorithm, which is responsible for translating data from the former into instructions for the latter. In this work, we propose to bring machine learning techniques to bear upon the challenges of blood glucose control. Specifically, we employ reinforcement learning to learn an optimal insulin policy. Learning is generalized using nonparametric regression with functional features, exploiting information contained in the shape of the glucose curve. Our algorithm is model-free, data-driven and personalized. In-silico simulations with T1D models demonstrate the potential of the proposed algorithm.