Fully-Automated Patient-Agnostic Diabetes Management with Deep Reinforcement Learning

Type 1 diabetes is a chronic metabolic disease that requires regular insulin injections to regulate blood glucose levels. Recently, traditional manual approaches to diabetes management have been revolutionized by the use of continuous glucose monitors and insulin pumps to automate this process as much as possible. However, these approaches either require manual meal announcements, which is subject to human error, or need to be fine-tuned on a specific patient, which is challenging and risky. In this paper, we present a fully-automated patient-agnostic model that does not require any human intervention or patient-specific adjustments. Specifically, we use a deep reinforcement learning algorithm (Soft Actor-Critic) trained on an improved version of the FDA-approved UVA/Padova simulator for type 1 diabetes patients. By adding standardized meal scenarios to the simulator, we are making our results reproducible and comparable for future studies. Our model is compared to several baseline methods and evaluated on unseen patients by employing a rigorous train/validation/test split of the virtual cohort. Our patient-agnostic model achieves a score of 80.3% time in range compared to 77.3% for the patient-specific PID baseline and 72.7% for the state-of-the-art approach by Fox et al. [1]. Our approach has the potential to increase the ease and safety of using a fully-automated system for type 1 diabetes management in a real-world setting. Our code, models, and the improved simulator are available at https://github.com/Tom-Milton/RLAP.