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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, 2021, 47(x): 1−14 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, 2021, 47(x): 1−14 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, 2021, 47(x): 1−14 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, 2021, 47(x): 1−14 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及在生物医学、机器人及工业过程中的应用. 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控制、估计与学习以及闭环给药系统控制算法设计与实现. 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, 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, 北京大学糖尿病中心共同主任. 主要研究方向为糖尿病分子病因学和转化医学. 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), 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 & synthesis of advanced sampled-data control systems, with applications to biomedical engineering, robotics and motion systems. 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

    LIU Wei Physician in the Endocrinology Department of Peking University People's Hospital. Her research interest covers 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, electro-hydraulic servo/proportional control, test experiment and load simulation, and robotic control

    JI Li-Nong Director of the Department of Endocrinology, Peking University People's Hospital, and co-director of Peking University Diabetes Center. 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、有效决策. 针对这一问题, 本文设计了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(Food and drug administration, FDA)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. 本文工作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, 也为我国糖尿病患者血糖管理水平的有效改善提供精准医学治疗手段.
    1)  收稿日期 2021-01-22 录用日期 2021-04-23 Manuscript received January 22, 2021; accepted April 23, 2021 国家自然科学基金 (61973030), 北京市自然科学基金 (4192052)资助 Supported by National Natural Science Foundation of China(61973030), Natural Science Grant of Beijing (4192052) 本文责任编委 Recommended by Associate Editor 1. 北京理工大学自动化学院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 1. State Key Laboratory of Intelligent Control and Decision for Complex Systems, School of Automation, Beijing Institute of
    2)  Technology 100081 2. Peking University People's Hospital, Beijing 100044
  • 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 ($r=1$)

    图  5  前期剂量偏低($ 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 ($ r = 0.5 $)

    图  6  前期剂量偏高($ 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 ($ r = 1.5 $)

    图  7  膳食扰动下本方法与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 Run-to-Run 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 parameter 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, $
    $ 160,1450,150,150]^\top $
    $ \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  前期剂量正常情形下血糖控制评估结果

    Table  2  Evaluation results of the glucose control in the case of normal boluses in the early stage

    仿真情形 前期餐前剂量正常($ r = 1 $)
    评价指标 标准方法 本方法 $ p $ 值
    时间百分比
    <54 mg/dL 0.0 (0.0) 0.0 (0.0) 1.000
    <70 mg/dL 0.0 (0.0) 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) 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  前期剂量偏低/偏高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

    仿真情形 前期餐前剂量偏少($ r = 0.5 $) 前期餐前剂量偏多($ r = 1.5 $)
    评价指标 标准方法 本方法 $ p $ 值 标准方法 本方法 $ p $ 值
    时间百分比
    <54 mg/dL 0.0 (0.0) 0.0 (0.0) 1.000 1.6 (3.8) 0.0 (0.0) 0.031
    <70 mg/dL 0.0 (0.0) 0.0 (0.0) 1.000 6.5 (7.8) 0.0 (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 (0.0) 0.031 0.0 (0.0) 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
    07:00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

    仿真情形 膳食扰动
    评价指标 R2R 本方法 $ p $ 值
    时间百分比
    $< $54 mg/dL 0.0 (0.7) 0.0 (0.0) 0.250
    $< $70 mg/dL 3.6 (8.3) 0.0 (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) 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
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