Neural network-based model predictive control for type 1 diabetic rats on artificial pancreas system
Neural network-based model predictive control for type 1 diabetic rats on artificial pancreas system
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DOI：
10.1007/s11517-018-1872-6
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发表时间：
2019-01-01
影响因子：
3.2
通讯作者：
Kwon, Guim
中科院分区：
文献类型：
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作者：
Bahremand, Saeid;Ko, Hoo Sang;Kwon, Guim
人工胰腺系统（APS）是治疗糖尿病患者的一种可行的选择。然而，研究人员还没有最终确定APS的最佳控制方法。由于胰岛素吸收和作用的内部/内部变异性，需要个体化算法来控制每个患者的血糖水平（BGL）。为此，我们开发了基于人工神经网络（ANN）的模型预测控制（MPC），它结合了基于输入的BGL预测的ANN和基于ANN的BGL控制的MPC（NN-MPC）。首先，我们为糖尿病大鼠开发了一个数学模型，该模型通过拟合APS收集的经验数据（包括BGL数据、胰岛素注射和食物摄入）来识别个体虚拟受试者。然后，虚拟主题被用来生成训练人工神经网络的数据集。NN-MPC确定控制动作（胰岛素注射）的基础上BGL预测的ANN。为了评估NN-MPC，我们进行了实验，使用四个虚拟主题下三种不同的情况。总体而言，NN-MPC将BGL维持在正常范围内约90%的时间，与预期BGL的平均绝对偏差为4.7 mg/dl。我们的研究结果表明，神经网络MPC可以提供特定于主题的BGL控制与闭环APS。
Artificial pancreas system (APS) is a viable option to treat diabetic patients. Researchers, however, have not conclusively determined the best control method for APS. Due to intra-/inter-variability of insulin absorption and action, an individualized algorithm is required to control blood glucose level (BGL) for each patient. To this end, we developed model predictive control (MPC) based on artificial neural networks (ANNs), which combines ANN for BGL prediction based on inputs and MPC for BGL control based on the ANN (NN-MPC). First, we developed a mathematical model for diabetic rats, which was used to identify individual virtual subjects by fitting to empirical data collected through an APS, including BGL data, insulin injection, and food intake. Then, the virtual subjects were used to generate datasets for training ANNs. The NN-MPC determines control actions (insulin injection) based on BGL predicted by the ANN. To evaluate the NN-MPC, we conducted experiments using four virtual subjects under three different scenarios. Overall, the NN-MPC maintained BGL within the normal range about 90% of the time with a mean absolute deviation of 4.7mg/dl from a desired BGL. Our findings suggest that the NN-MPC can provide subject-specific BGL control in conjunction with a closed-loop APS.