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  

Bayesian Learning Based Optimization of Meal Bolus Dosage for Intelligent Glucose Management

作　　者：史大威 蔡德恒 刘蔚[2] 王军政[1] 纪立农[2] SHI Da-Wei;CAI De-Heng;LIU Wei;WANG Jun-Zheng;JI Li-Nong(State Key Laboratory of Intelligent Control and Decision for Complex Systems,Beijing Institute of Technology,Beijing 100081;People's Hospital,Peking University,Beijing 100044)

机构地区：[1]北京理工大学复杂系统智能控制与决策国家重点实验室,北京100081 [2]北京大学人民医院,北京100044

出　　处：《自动化学报》2023年第9期1915-1927,共13页Acta Automatica Sinica

基　　金：国家自然科学基金(61973030);北京市自然科学基金(4192052)资助。

摘　　要：餐前胰岛素剂量精准决策是改善糖尿病患者血糖管理的关键.临床治疗中胰岛素剂量调整一般在较短时间内完成,具有典型的小样本特征;数据驱动建模在该情形下无法准确学习患者餐后血糖代谢规律,难以确保胰岛素剂量的安全和有效决策.针对这一问题,设计一种临床经验辅助的餐前胰岛素剂量自适应优化决策框架,构建高斯过程血糖预测模型和模型有效性在线评估机制,提出基于历史剂量和临床经验决策约束的贝叶斯优化方法,实现小样本下餐后血糖轨迹的安全预测和餐前胰岛素注射剂量的优化决策.该方法的安全性和有效性通过美国食品药品监督管理局接受的UVA/Padova T1DM平台测试结果和1型糖尿病患者实际临床数据决策结果充分验证.可为餐前胰岛素剂量智能决策及临床试验提供方法基础和技术支持,也为中国糖尿病患者血糖管理水平的有效改善,提供了精准医学治疗手段.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.

关 键 词：餐前剂量决策 数据驱动建模 贝叶斯优化 临床经验 临床数据验证 

分 类 号：R587.1[医药卫生—内分泌] TP18[医药卫生—内科学]