An Insulin-Sensitivity-Aware Meal-Bolus Decision Method Based on Event-Triggered Adaptive Dynamic Programming

Postprandial glucose management is crucial for patients with diabetes. However, the currently adopted meal-bolus decision algorithms normally rely on subject-specific parameters and are impacted by time-varying insulin requirements caused by behavioral or metabolic variations. In this work, a data-driven meal-bolus decision method with an insulin-sensitivity-aware event-triggered learning mechanism is proposed. Specifically, the algorithm is developed within the framework of adaptive dynamic programming, which utilizes a Gaussian process regression approach to construct the model network and applies two shallow neural networks to construct the critic and action networks. To allow the algorithm to quickly perceive and respond to the variation of glycemic dynamics, different utility functions characterized by insulin-sensitivity-dependent penalty terms are constructed and switched based on the estimated insulin sensitivity conditions. The effectiveness of the proposed method is evaluated by in silico experiments utilizing the 10-adult cohort from the FDA-accepted UVa/Padova T1DM simulator. For the scenario of decreased insulin sensitivity, the proposed method leads to statistically significant improvements in terms of the percent time in euglycemia 70-180 mg/dL (86.2% vs. 72.4%, $p=0.025$ ) and the mean glucose (142.3 mg/dL vs. 155.3 mg/dL, $p=0.020$ ) without increasing the risk of hypoglycemia compared with the standard bolus calculator. Besides, an advisor-mode analysis using clinical data for subjects who initially exhibited significant hyperglycemia upon starting insulin therapy is conducted, which proves its effectiveness and safety in practical applications. Note to Practitioners—This work is motivated by the issue of postprandial blood glucose management, which is indispensable to diabetes patients. An effective meal-bolus decision method can help individuals resist the surge in blood glucose levels caused by carbohydrate ingestions and reduce the risk of hyperglycemia. However, for the state-of-the-art bolus decision algorithms, the inter/intra-subject variations of physiological parameters and the fluctuating insulin requirements caused by physical activity, stress, or emotional change are the two main causes of performance degeneration. To this end, we propose an insulin-sensitivity-aware event-triggered learning approach to meal bolus optimization. Specifically, without explicitly adjusting the subject-specific parameters, an adaptive dynamic programming decision framework is designed to perform the bolus decision based on the data-driven network learning and the iteration of control law and cost function. Besides, the estimated insulin sensitivity information is utilized in the learning phase of the decision framework to optimize the ultimate bolus, bolus dosage so as to make the algorithm quickly respond to the variation of insulin requirements. The in silico experiments and clinical data advisor-mode analysis demonstrate the effectiveness of the proposed algorithm and its feasibility in clinical application for postprandial blood glucose management.