Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters.
Development of a multi-parametric model predictive control algorithm for insulin delivery in type 1 diabetes mellitus using clinical parameters.
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DOI：
10.1016/j.jprocont.2010.10.003
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发表时间：
2011-03-01
影响因子：
4.2
通讯作者：
Doyle, F. J., III
中科院分区：
文献类型：
--
作者：
Percival, M. W.;Wang, Y.;Grosman, B.;Dassau, E.;Zisser, H.;Jovanovic, L.;Doyle, F. J., III
关键词：
提出了一种多参数模型预测控制(MpMPC)算法，用于1型糖尿病(T1 DM)患者的皮下胰岛素给药，该算法计算高效，对胰岛素敏感性的变化具有鲁棒性，并且对用户的负担最小。系统识别是通过在UVA/Padova模拟器上对T1 DM成年受试者进行适合步行条件的脉冲反应测试来实现的。还研究了使用现成的临床参数进行系统识别的另一种方法。使用主治医生可用的典型临床参数，在算法公式中明确地包括安全约束。在每天消耗200g碳水化合物的情况下进行了闭环模拟。控制器的稳健性是通过受试者/模型不匹配的场景来评估的，该场景解决了胰岛素敏感性和食物大小的日常同时变化，并添加了标准偏差为10%的高斯白噪声。对于3h的预测时段，二阶加时滞传递函数模型对验证数据的平均(变异系数)均方误差(RMSE)为26 mg/dL(19%)。由此产生的控制法维持了低风险的低血糖指数，而没有任何关于90%的受试者碳水化合物摄入量的信息。因此，具有临床意义参数的低阶线性模型为模型预测控制算法控制血糖提供了足够的信息。使用临床知识作为安全约束可以减少低血糖事件，当明确用作控制器模型时，同样的知识可以进一步改善血糖控制。由此得到的mpMPC算法足够紧凑，可以在简单的电子设备上实现。
A multi-parametric model predictive control (mpMPC) algorithm for subcutaneous insulin delivery for individuals with type 1 diabetes mellitus (T1DM) that is computationally efficient, robust to variations in insulin sensitivity, and involves minimal burden for the user is proposed. System identification was achieved through impulse response tests feasible for ambulatory conditions on the UVa/Padova simulator adult subjects with T1DM. An alternative means of system identification using readily available clinical parameters was also investigated. A safety constraint was included explicitly in the algorithm formulation using clinical parameters typical of those available to an attending physician. Closed-loop simulations were carried out with daily consumption of 200 g carbohydrate. Controller robustness was assessed by subject/model mismatch scenarios addressing daily, simultaneous variation in insulin sensitivity and meal size with the addition of Gaussian white noise with a standard deviation of 10%. A second-order-plus-time-delay transfer function model fit the validation data with a mean (coefficient of variation) root-mean-square-error (RMSE) of 26 mg/dL (19%) for a 3 h prediction horizon. The resulting control law maintained a low risk Low Blood Glucose Index without any information about carbohydrate consumption for 90% of the subjects. Low-order linear models with clinically meaningful parameters thus provided sufficient information for a model predictive control algorithm to control glycemia. The use of clinical knowledge as a safety constraint can reduce hypoglycemic events, and this same knowledge can further improve glycemic control when used explicitly as the controller model. The resulting mpMPC algorithm was sufficiently compact to be implemented on a simple electronic device.
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