Online Optimization Approach for Calculating Basal Insulin Doses for Individuals With Type-2 Diabetes

作     者：Al Ahdab, Mohamad Knudsen, Torben Stoustrup, Jakob Leth, John 

作者机构：Aalborg Univ Dept Elect Syst Sect Automation & Control DK-9220 Aalborg Denmark 

出 版 物：《IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY》 (IEEE Trans Control Syst Technol)

年 卷 期：2025年

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 0811[工学-控制科学与工程] 

基　　金：Innovation Fund Denmark (IFD) Grand Solution Project ADAPT-T2D [9068-00056B] 

主　　题：Insulin Physiology Glucose Vectors Optimization Noise measurement Symbols Estimation Diabetes Noise human-in-the-loop online optimization 

摘      要：This article presents a novel and computationally efficient approach for determining long-acting insulin doses in individuals with type-2 diabetes (T2D) without relying on a specific physiological model. The main objective is maintaining safe blood glucose (BG) levels within a targeted range. To achieve this, the strategy optimizes the parameters of a designated control law through an online optimization method combined with a gradient estimation technique that relies on noisy evaluations of an objective function. Two distinct control laws are employed and compared, along with two gradient estimation methods: recursive least squares (RLSs) and a one-point residual feedback method. Moreover, we explore the potential advantages of integrating continuous glucose monitoring (CGM) readings into the proposed framework for basal insulin dosing in T2D individuals. This approach is compared with the use of self-monitored BG (SMBG) devices alone. Through simulations with three different models, the proposed insulin calculation framework s performance surpasses existing strategies from the literature. The results also show that using CGM readings as feedback for long-acting insulin dose calculations significantly lowers the risk of hypoglycemia. Furthermore, the one-point residual feedback method outperformed RLS while. Finally, through the simulation results, we highlight the importance of the choice of the control law in the online optimization framework.