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.
Bibliographical noteFunding Information:
The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was funded by the National Institutes of Health National Center for Advancing Translational Sciences (UL1TR002494) and the University of Minnesota Academic Health Center (ACH-FRD-17-08 to L.S.C.). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health’s National Center for Advancing Translational Sciences.
© 2022 Diabetes Technology Society.
- continuous glucose monitoring
- gated recurrent unit
- glucose forecast
- neural network
PubMed: MeSH publication types
- Journal Article