Free-living inferential modeling of blood glucose level using only noninvasive inputs
Free-living inferential modeling of blood glucose level using only noninvasive inputs
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DOI：
10.1016/j.jprocont.2009.09.008
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发表时间：
2010-02-01
影响因子：
4.2
通讯作者：
Franke, Warren E.
中科院分区：
文献类型：
--
作者：
Rollins, Derrick K.;Bhandari, Nidhi;Franke, Warren E.
这项工作的目标是提出一种因果关系建模方法，能够在很长一段时间内使用大量高度相关的非侵入性输入变量准确推断血糖水平。这些模型可以通过先进的基于模型的控制技术提供洞察力，以改善葡萄糖监测和葡萄糖调节。使用连续25天在自由生活条件下收集的2型糖尿病（T2 D）受试者的真实的数据证明了这种方法的有效性。该模型被确定和测试使用11个变量，其中包括三个食物变量以及几个活动和压力变量。该模型使用20天的数据进行训练，并使用5天的数据进行验证。这给出了0.70的拟合相关系数和平均绝对误差（AAE）（即，测量的葡萄糖浓度减去建模的葡萄糖浓度的绝对值的平均值）为13.3 mg/dL。对于重复测量，该AAE结果显著优于受试者的个人血糖仪AAE 15.3 mg/dL。由爱思唯尔有限公司发布
The goal of this work is to present a causation modeling methodology with the ability to accurately infer blood glucose levels using a large set of highly correlated noninvasive input variables over an extended period of time. These models can provide insight to improve glucose monitoring, and glucose regulation through advanced model-based control technologies. The efficacy of this approach is demonstrated using real data from a type 2 diabetic (T2D) subject collected under free-living conditions over a period of 25 consecutive days. The model was identified and tested using eleven variables that included three food variables as well as several activity and stress variables. The model was trained using 20 days of data and validated using 5 days of data. This gave a fitted correlation coefficient of 0.70 and an average absolute error (AAE) (i.e., the average of the absolute values for the measured glucose concentration minus modeled glucose concentration) of 13.3 mg/dL for the validation data. This AAE result was significantly better than the Subject's personal glucose meter AAE of 15.3 mg/dL for replicated measurements. Published by Elsevier Ltd.