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Neural Networks With Gated Recurrent Units Reduce Glucose Forecasting Error Due to Changes in Sensor Location
Aaron P Tucker1, Arthur G Erdman1, Pamela J Schreiner2
1Earl E. Bakken Medical Devices Center, University of Minnesota, Minneapolis, MN, USA.
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 (P < .05).
Conclusion:
We observe that GRU NNs can mitigate error in glucose prediction due to differences in CGM location.
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