HypoTreat: Reducing Hypoglycemia in Artificial Pancreas Simulation

Maintaining optimal blood glucose levels is paramount in diabetes management. Currently, patients are tasked with deciding when and how much insulin to administer. In contrast, reinforcement learning offers a closed-loop control system that automates this process. In this study, we present a reinforcement learning framework tailored for personalized blood glucose management, employing in silico patients. The agent dynamically adjusts insulin dosages in response to real-time glucose levels and patient-specific characteristics. We introduce HypoTreat, an innovative extension to the model that incorporates the 15-15 rule. This addition simulates the real-life patient’s behavior by temporarily opening the closed-loop, permitting virtual patients to consume food when their blood glucose level falls below 70, thus, preventing hypoglycemia.While the scenario of opening the closed-loop is undesirable, our findings reveal its efficacy in averting catastrophic hypoglycemia without increasing time spent in hyperglycemia. By introducing HypoTreat in training the average time spent in severe hypoglycemia has been reduced by 97.4%. Meanwhile, the in silico patients remain over 80% of their time within the target blood glucose range. This demonstrates the potential of our simulation-driven approach to enhance blood glucose control by intelligently incorporating real-life behavior, providing a safer and more effective solution for diabetes management.