面向智能血糖管理的餐前胰岛素剂量贝叶斯学习优化方法

Precise decision of preprandial insulin bolus is of crucial importance to achieving enhanced glucose management for patients with diabetes. Insulin dosage adjustment in current clinical practice normally needs to be completed in a relatively short period of time and thus the data size is typically small. It would be consequently challenging to accurately learn the postprandial glucose metabolism through data-driven modelling techniques and to further ensure the safe and efficient insulin dosage decision. For this problem, a clinical-experience-assisted adaptive preprandial insulin bolus decision framework is proposed in this work. To achieve safe prediction of the postprandial glucose traces and optimal decision of the preprandial insulin dosage with limited training data, a Gaussian process based glucose prediction model and an online model efficiency assessment mechanism are constructed, and a Bayesian optimization method with historical data exploitation and clinical-experience guided decision constraints is proposed. The safety and effectiveness of the proposed method are extensively validated through in silico results of the US Food and Drug Administration accepted UVA/Padova T1DM simulator and advisory mode analysis based on clinical data from a subject with type 1 diabetes. The obtained results provide methodological and technical support for intelligent meal bolus decision and the forthcoming clinical studies, and introduce a precision medicine solution to effectively improved glucose management for Chinese patients with diabetes mellitus.