带有门控循环单元的神经网络可减少因传感器位置变化导致的血糖预测误差。
Neural Networks With Gated Recurrent Units Reduce Glucose Forecasting Error Due to Changes in Sensor Location.
作者信息
Tucker Aaron P, Erdman Arthur G, Schreiner Pamela J, Ma Sisi, Chow Lisa S
机构信息
Earl E. Bakken Medical Devices Center, University of Minnesota, Minneapolis, MN, USA.
Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA.
出版信息
J Diabetes Sci Technol. 2024 Jan;18(1):124-134. doi: 10.1177/19322968221100839. Epub 2022 Jun 4.
BACKGROUND
Continuous glucose monitors (CGMs) have become important tools for providing estimates of glucose to patients with diabetes. Recently, neural networks (NNs) have become a common method for forecasting glucose values using data from CGMs. One method of forecasting glucose values is a time-delay feedforward (FF) NN, but a change in the CGM location on a participant can increase forecast error in a FF NN.
METHODS
In response, we examined a NN with gated recurrent units (GRUs) as a method of reducing forecast error due to changes in sensor location.
RESULTS
We observed that for 13 participants with type 2 diabetes wearing blinded CGMs on both arms for 12 weeks (FreeStyle Libre Pro-Abbott), GRU NNs did not produce significantly different errors in glucose prediction due to sensor location changes ( < .05).
CONCLUSION
We observe that GRU NNs can mitigate error in glucose prediction due to differences in CGM location.
背景
连续血糖监测仪（CGM）已成为向糖尿病患者提供血糖估计值的重要工具。最近，神经网络（NN）已成为一种使用CGM数据预测血糖值的常用方法。预测血糖值的一种方法是时延前馈（FF）神经网络，但参与者身上CGM位置的变化会增加FF神经网络中的预测误差。
方法
作为回应，我们研究了一种带有门控循环单元（GRU）的神经网络，作为减少因传感器位置变化而导致的预测误差的一种方法。
结果
我们观察到，对于13名2型糖尿病患者，他们双臂佩戴盲法CGM长达12周（FreeStyle Libre Pro-雅培），GRU神经网络并未因传感器位置变化而在血糖预测中产生显著不同的误差（<.05）。
结论
我们观察到GRU神经网络可以减轻因CGM位置差异而导致的血糖预测误差。
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