Practical issues in the identification of empirical models from simulated type 1 diabetes data
Practical issues in the identification of empirical models from simulated type 1 diabetes data
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DOI：
10.1089/dia.2007.0202
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发表时间：
2007-10-01
影响因子：
5.4
通讯作者：
Seborg, Dale E.
中科院分区：
文献类型：
--
作者：
Finan, Daniel A.;Zisser, Howard;Seborg, Dale E.
背景资料：一种用于人工6-细胞的基于模型的控制器，其基于可用的葡萄糖测量、胰岛素输注和膳食信息以及未来葡萄糖趋势的模型预测来自动调节血糖水平。因此，识别简单，准确的模型在人工β-cell.Methods的发展中起着重要的作用：葡萄糖数据模拟的非线性生理模型的I型糖尿病被用来识别线性动态模型的两种类型：自回归外源性输入（ARX）和输出误差（OE）模型。模型输入是膳食碳水化合物和外源性胰岛素，在实践中，它们通常同时以相同的比例给药，即，胰岛素与碳水化合物的比例深入探讨了将这些输入建模为脉冲与时间平滑曲线（“转换输入”）的效果。基于模型描述识别它们的数据的能力来评估模型（即，校准数据）以及独立数据（即，验证数据）。结果：一般而言，最佳模型使用转换输入（ARX模型的R-Cal（2）= 71%，OE模型的R-Cal（2）= 78%）比使用脉冲输入（ARX模型的R-Cal（2）= 14%，OE模型的R-Cal（2）= 70%）的C C 2更准确地描述其校准数据。唯一一个能够产生一致准确验证拟合的模型/输入组合是使用转换输入的ARX模型（39%）。
Background: A model-based controller for an artificial 6-cell automatically regulates blood glucose levels based on available glucose measurements, insulin infusion and meal information, and model predictions of future glucose trends. Thus, the identification of simple, accurate models plays an important role in the development of an artificial beta-cell.Methods: Glucose data simulated from a nonlinear physiological model of type I diabetes are used to identify linear dynamic models of two types: autoregressive exogenous input (ARX) and output-error (OE) models. The model inputs are meal carbohydrates and exogenous insulin, which in practice are often administered simultaneously and in the same ratio, i.e., the insulin-to-carbohydrate ratio. The effect of modeling these inputs as impulses versus time-smoothed profiles ("transformed inputs") is explored in depth. The models are evaluated based on their ability to describe the data from which they were identified (i.e., calibration data) as well as independent data (i.e., validation data).Results: In general, the best models described their calibration data more accurately using transformed inputs (R-Cal(2) = 71% for the ARX models and R-Cal(2) = 78% for the OE models) than C C 2 , using impulse inputs (R-Cal(2) = 14% for the ARX models and R-Cal(2) = 70% for the OE models). The only model/input combination that resulted in consistently accurate validation fits was the ARX models using transformed inputs (39%