Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control.
Enhanced Model Predictive Control (eMPC) Strategy for Automated Glucose Control.
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DOI：
10.1021/acs.iecr.6b02718
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发表时间：
2016-11-23
影响因子：
4.2
通讯作者：
Doyle, Francis J., III
中科院分区：
文献类型：
--
作者：
Lee, Joon Bok;Dassau, Eyal;Gondhalekar, Ravi;Seborg, Dale E.;Pinsker, Jordan E.;Doyle, Francis J., III
开发一种有效的人工胰腺 (AP) 控制器来自主向 1 型糖尿病患者输送胰岛素是一项艰巨的任务。在本文中，提出了对经过临床验证的 AP 模型预测控制器 (MPC) 的三项增强功能，以解决自动化血糖控制面临的主要挑战，然后通过计算机测试和临床试验进行评估。首先，MPC 中使用的胰岛素-血糖动力学核心模型通过受医学启发的个性化方案进行了扩展，以改善控制器在面对胰岛素敏感性的个体间和个体内差异时的反应。接下来，低血糖与高血糖的短期后果的不对称性质被纳入 MPC 成本函数的不对称权重中。最后，提出了一种增强的动态机载胰岛素算法，以最大限度地减少由于伴随胰岛素悬浮的救援碳水化合物负荷导致血糖快速上升后控制器引起的低血糖的可能性。每一项进步都通过基于新的临床方案的计算机试验进行单独和一致的评估，该方案结合了诱导高血糖和低血糖来测试稳健性。这些进步还通过临床数据的咨询模式（模拟）测试进行评估。这三项改进的结合显示，与非个性化控制器相比，在所有指标上没有任何增强的情况下，性能在统计上显着提高，在 70-180 mg/dL 安全血糖范围（76.9% 对 68.8%）和 80-140 mg/dL 正常血糖范围（48.1% 对 44.5%）内的时间增加，而低血糖情况没有统计上显着增加。所提出的改进为 AP 应用提供了安全的控制操作，个性化并提高了控制器性能，而无需大量的模型识别过程。
Development of an effective artificial pancreas (AP) controller to deliver insulin autonomously to people with type 1 diabetes mellitus is a difficult task. In this paper, three enhancements to a clinically validated AP model predictive controller (MPC) are proposed that address major challenges facing automated blood glucose control, and are then evaluated by both in silico tests and clinical trials. First, the core model of insulin-blood glucose dynamics utilized in the MPC is expanded with a medically inspired personalization scheme to improve controller responses in the face of inter- and intra-individual variations in insulin sensitivity. Next, the asymmetric nature of the short-term consequences of hypoglycemia versus hyperglycemia is incorporated in an asymmetric weighting of the MPC cost function. Finally, an enhanced dynamic insulin-on-board algorithm is proposed to minimize the likelihood of controller-induced hypoglycemia following a rapid rise of blood glucose due to rescue carbohydrate load with accompanying insulin suspension. Each advancement is evaluated separately and in unison through in silico trials based on a new clinical protocol, which incorporates induced hyper- and hypoglycemia to test robustness. The advancements are also evaluated in an advisory mode (simulated) testing of clinical data. The combination of the three proposed advancements show statistically significantly improved performance over the nonpersonalized controller without any enhancements across all metrics, displaying increased time in the 70–180 mg/dL safe glycemic range (76.9 versus 68.8%) and the 80–140 mg/dL euglycemic range (48.1 versus 44.5%), without a statistically significant increase in instances of hypoglycemia. The proposed advancements provide safe control action for AP applications, personalizing and improving controller performance without the need for extensive model identification processes.
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影响因子：
7.7
作者：
ALBISSER, AM;LEIBEL, BS;GANDER, R
通讯作者：
GANDER, R
影响因子：
16.2
作者：
Kovatchev BP;Renard E;Cobelli C;Zisser HC;Keith-Hynes P;Anderson SM;Brown SA;Chernavvsky DR;Breton MD;Mize LB;Farret A;Place J;Bruttomesso D;Del Favero S;Boscari F;Galasso S;Avogaro A;Magni L;Di Palma F;Toffanin C;Messori M;Dassau E;Doyle FJ 3rd
通讯作者：
Doyle FJ 3rd
DOI：
10.1109/tbme.2014.2323248
发表时间：
2014-10
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Jacobs PG;El Youssef J;Castle J;Bakhtiani P;Branigan D;Breen M;Bauer D;Preiser N;Leonard G;Stonex T;Ward WK
通讯作者：
Ward WK
DOI：
10.1111/dom.12549
发表时间：
2015-12
期刊：
Diabetes, obesity & metabolism
影响因子：
--
作者：
Elleri D;Biagioni M;Allen JM;Kumareswaran K;Leelarathna L;Caldwell K;Nodale M;Wilinska ME;Haidar A;Calhoun P;Kollman C;Jackson NC;Umpleby AM;Acerini CL;Dunger DB;Hovorka R
通讯作者：
Hovorka R
影响因子：
16.2
作者：
Nimri, Revital;Muller, Ido;Phillip, Moshe
通讯作者：
Phillip, Moshe