Zhu, T; Li, K; Georgiou, P; (2020) Personalized Dual-Hormone Control for Type 1 Diabetes Using Deep Reinforcement Learning. In: Shaban-Nejad, A and Michalowski, M and Buckeridge, DL, (eds.) Explainable AI in Healthcare and Medicine. (pp. pp. 45-53). Springer: Cham, Switzerland.

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Abstract

We introduce a dual-hormone control algorithm for people with Type 1 Diabetes (T1D) which uses deep reinforcement learning (RL). Specifically, double dilated recurrent neural networks are used to learn the control strategy, trained by a variant of Q-learning. The inputs to the model include the real-time sensed glucose and meal carbohydrate content, and the outputs are the actions necessary to deliver dual-hormone (basal insulin and glucagon) control. Without prior knowledge of the glucose-insulin metabolism, we develop a data-driven model using the UVA/Padova Simulator. We first pre-train a generalized model using long-term exploration in an environment with average T1D subject parameters provided by the simulator, then adopt importance sampling to train personalized models for each individual. In-silico, the proposed algorithm largely reduces adverse glycemic events, and achieves time in range, i.e., the percentage of normoglycemia, 93% for the adults and 83% for the adolescents, which outperforms previous approaches significantly. These results indicate that deep RL has great potential to improve the treatment of chronic diseases such as diabetes.

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