Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 193229682211008 |
| Journal | Journal of Diabetes Science and Technology |
| Early online date | Jun 2022 |
| DOIs | |
| State | E-pub ahead of print - Jun 2022 |
Bibliographical note
Funding 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.
Publisher Copyright:
© 2022 Diabetes Technology Society.
Keywords
- continuous glucose monitoring
- gated recurrent unit
- glucose forecast
- neural network
PubMed: MeSH publication types
- Journal Article
