As of 2017 CDC report, more than 100 million U.S. adults are now living with diabetes or prediabetes. This disease results from high blood glucose (blood sugar) due to an inability to properly derive energy from food, primarily in the form of glucose. Effective diabetic care for insulin dependent Type-1 patients requires that a healthy-level of glucose is maintained throughout the day with minimal fluctuations in either direction. The goal of insulin dependent diabetic care is to administer appropriate amount of insulin at appropriate time such that glucose level is maintained at near target level without reaching hypo or hyper level.
This research work proposes and explores the effectiveness of Deep Reinforcement Learning models as the insulin controller for Type 1 diabetes. Given the nature of the insulin-glucose dynamics, Reinforcement Learning based approaches seem to be more suitable compared to model driven controller typically employed for the diabetic care. Specifically, adapting the Deep Deterministic Policy Gradient (DDPG) algorithm, a Deep Reinforcement Learning based insulin controller is proposed and analyzed to study its efficacy in achieving better glucose control. Given that DDPG is a model-free, off-policy actor-critic algorithm using deep function approximators that can learn policies in high-dimensional, continuous action spaces, we develop the insulin controller using DDPG algorithm
We implement our approach in SimGlucose environment, a recently proposed Reinforcement Learning based software platform that supports Open AI Gym to evaluate our proposed controller. We compare the performance of DDPG based insulin controller that with widely used Padova model based insulin controller. Our simulation driven evaluations indicate that DDPG based controllers are able to better react and control blood sugar fluctuations compared to model driven controllers. While further evaluations with real datasets are required, the preliminary results indicate that Deep Reinforcement Learning based insulin controllers are promising candidates for better glucose control for Type-1 diabetic care.