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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

史大威 蔡德恒 刘蔚 王军政 纪立农

史大威, 蔡德恒, 刘蔚, 王军政, 纪立农. 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. 自动化学报, 2023, 49(9): 1915−1927 doi: 10.16383/j.aas.c210067
引用本文: 史大威, 蔡德恒, 刘蔚, 王军政, 纪立农. 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. 自动化学报, 2023, 49(9): 1915−1927 doi: 10.16383/j.aas.c210067
Shi Da-Wei, Cai De-Heng, Liu Wei, Wang Jun-Zheng, Ji Li-Nong. Bayesian learning based optimization of meal bolus dosage for intelligent glucose management. Acta Automatica Sinica, 2023, 49(9): 1915−1927 doi: 10.16383/j.aas.c210067
Citation: Shi Da-Wei, Cai De-Heng, Liu Wei, Wang Jun-Zheng, Ji Li-Nong. Bayesian learning based optimization of meal bolus dosage for intelligent glucose management. Acta Automatica Sinica, 2023, 49(9): 1915−1927 doi: 10.16383/j.aas.c210067

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

doi: 10.16383/j.aas.c210067
基金项目: 国家自然科学基金(61973030), 北京市自然科学基金(4192052) 资助
详细信息
    作者简介:

    史大威:北京理工大学自动化学院教授. 主要研究方向为复杂采样控制系统分析与设计, 生物医学工程控制, 机器人和工业过程控制. 本文通信作者. E-mail: daweishi@bit.edu.cn

    蔡德恒:北京理工大学自动化学院博士研究生. 主要研究方向为事件触发的采样数据控制, 状态估计与机器学习和闭环给药系统控制. E-mail: dehengcai@bit.edu.cn

    刘蔚:北京大学人民医院内分泌科主治医师. 主要研究方向为糖尿病分子病因学. E-mail: liuwei850217@163.com

    王军政:北京理工大学自动化学院教授. 主要研究方向为运动驱动与控制, 电液伺服/比例控制, 试验测试与负载模拟和机器人控制. E-mail: wangjz@bit.edu.cn

    纪立农:北京大学人民医院内分泌科主任医师. 主要研究方向为糖尿病分子病因学, 转化医学. E-mail: jiln@bjmu.edu.cn

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

Funds: Supported by National Natural Science Foundation of China (61973030) and Natural Science Grant of Beijing (4192052)
More Information
    Author Bio:

    SHI Da-Wei Professor at the School of Automation, Beijing Institute of Technology. His research interest covers analysis and design of complex sampled-data control systems, control of biomedical engineering, robotics, and industrial process control. Corresponding author of this paper

    CAI De-Heng Ph.D. candidate at the School of Automation, Beijing Institute of Technology. His research interest covers event-triggered sampled-data control, state estimation and machine learning, and closed-loop drug delivery systems control

    LIU Wei Physician in the Department of Endocrinology, People's Hospital, Peking University. Her main research interest is molecular etiology of diabetes

    WANG Jun-Zheng Professor at the School of Automation, Beijing Institute of Technology. His research interest covers motion drive and control, elector-hydraulic servo/proportional control, test experiment and load simulation, and robotic control

    JI Li-Nong  Chief physician in the Department of Endocrinology, People's Hospital, Peking University. His research interest covers molecular etiology of diabetes and translational medicine

  • 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.
  • 图  1  餐前胰岛素剂量贝叶斯学习优化方法框架图

    Fig.  1  Block diagram of the Bayesian learning based optimization method for meal bolus

    图  2  惩罚强度变化示意图

    Fig.  2  Schematic diagram of the penalty changes

    图  3  临床经验决策规则

    Fig.  3  Clinical experience based decision rules

    图  4  前期剂量正常$r=1$情形下, 餐后血糖调节和剂量决策比较结果

    Fig.  4  Comparison of postprandial glucose regulation and dosage decision in the case of normal boluses in the early stage when$r=1$

    图  5  前期餐前剂量偏少$ r = 0.5 $情形下, 餐后血糖调节和剂量决策比较结果

    Fig.  5  Comparison of postprandial glucose regulation and dosage decision in the case of under-estimated boluses in the early stage when$ r = 0.5 $

    图  6  前期餐前剂量偏多$ r = 1.5 $情形下, 餐后血糖调节和剂量决策比较结果

    Fig.  6  Comparison of postprandial glucose regulation and dosage decision in the case of over-estimated boluses in the early stage when$ r = 1.5 $

    图  7  膳食扰动下, 本文方法与R2R方法餐后血糖调节和剂量决策比较结果

    Fig.  7  Comparison of postprandial glucose regulation and dosage decision in the case of meal disturbance between the proposed method and the R2R method

    图  8  前期血糖管理临床数据

    Fig.  8  The clinical data of the glucose management in the early stage

    图  9  基于临床数据的餐前剂量决策评估结果

    Fig.  9  The evaluation results for the meal bolus decision based on the clinical data

    表  1  方法参数

    Table  1  The parameters of the proposed method

    变量 含义
    $ \gamma $ 风险敏感参数 −2
    $ R $ 权重系数 10
    $ u_{\max} $ 最大剂量约束值 20
    $ M $ 最大优化次数 25
    $ {Q\mathit{\boldsymbol{}}}_b $ 基准惩罚矩阵 $\rm{diag}\{0.0025,\cdots, 0.0025,$
    $0.005,0.005\}$
    $ \Gamma $ 不对称惩罚强度 $ \{1,10,5,1\} $
    $ {G\mathit{\boldsymbol{}}}_r $ 餐后血糖控制目标 $[110,130,150,170, 160,$
    $150,150,150]^{\rm{T} }$
    $ \Delta u $ 评估增量 $ 2 $
    $ G_h $ 餐后血糖高阈值 $ 120 $
    $ G_l $ 餐后血糖低阈值 $ 80 $
    $ \Delta G $ 餐前血糖均值增量 $ 30 $
    下载: 导出CSV

    表  2  前期餐前剂量正常情形下$r = 1 $血糖控制评估结果 (%)

    Table  2  Evaluation results of the glucose control in the case of normal boluses in the early stage when$r=1 $ (%)

    评价指标 标准方法 本文方法 $ p $ 值
    < 54 mg/dL (%) 0 (0) 0 (0) 1.000
    < 70 mg/dL (%) 0 (0) 0 (0) 1.000
    70 ~ 180 mg/dL (%) 93.6 (7.5) 92.3 (6.6) 0.188
    > 250 mg/dL (%) 0 (0) 0 (0) 1.000
    血糖平均值(mg/dL) 133.2 (9.4) 132.5 (14.0) 1.000
    标准差(mg/dL) 30.2 (7.0) 30.3 (4.4) 0.037
    7:00血糖平均值(mg/dL) 109.8 (11.0) 109.3 (8.0) 0.195
    下载: 导出CSV

    表  3  前期餐前剂量偏少/偏多情形下血糖控制评估结果(%)

    Table  3  Evaluation results of the glucose control in the case of under-estimated/over-estimated boluses in the early stage (%)

    评价指标 前期餐前剂量偏少($ r = 0.5 $) 前期餐前剂量偏多($ r = 1.5 $)
    标准方法 本文方法 $ p $ 值 标准方法 本文方法 $ p $ 值
    < 54 mg/dL (%) 0 (0) 0 (0) 1.000 1.6 (3.8) 0 (0) 0.031
    < 70 mg/dL (%) 0 (0) 0 (0) 1.000 6.5 (7.8) 0 (1.4) 0.008
    70 ~ 180 mg/dL (%) 64.8 (23.6) 77.1 (18.4) 0.004 91.6 (6.9) 95.6 (2.6) 0.232
    > 250 mg/dL (%) 1.0 (4.5) 0 (0) 0.031 0 (0) 0 (0) 1.000
    血糖平均值 (mg/dL) 162.1 (29.2) 148.7 (16.8) 0.004 114.9 (9.0) 125.6 (10.5) 0.002
    标准差 (mg/dL) 37.9 (7.6) 35.8 (5.6) 0.010 29.4 (6.2) 30.9 (5.3) 0.049
    7:00血糖平均值 (mg/dL) 118.0 (20.0) 113.0 (9.5) 0.008 104.8 (10.0) 108.0 (12.5) 0.039
    下载: 导出CSV

    表  4  膳食扰动下血糖控制评估结果(%)

    Table  4  Evaluation results of the glucose control in the case of meal disturbance (%)

    评价指标 R2R 本文方法 $ p $ 值
    $< $54 mg/dL (%) 0 (0.7) 0 (0) 0.250
    $< $70 mg/dL (%) 3.6 (8.3) 0 (0) 0.016
    70 ~ 180 mg/dL (%) 87.7 (13.0) 89.1 (5.6) 0.625
    $>$250 mg/dL (%) 0 (0) 0 (0) 1.000
    血糖平均值(mg/dL) 123.8 (10.6) 131.3 (7.0) 0.006
    标准差(mg/dL) 36.0 (9.4) 34.9 (7.5) 0.432
    7:00血糖平均值(mg/dL) 102.8 (12.0) 103.8 (8.5) 0.004
    下载: 导出CSV
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  • 收稿日期:  2021-01-22
  • 录用日期:  2021-04-23
  • 网络出版日期:  2021-09-16
  • 刊出日期:  2023-09-26

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