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) |
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Pages (from-to) | 124-134 |
Number of pages | 11 |
Journal | Journal of Diabetes Science and Technology |
Volume | 18 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2024 |
Bibliographical note
Publisher Copyright:© 2022 Diabetes Technology Society.
Keywords
- continuous glucose monitoring
- gated recurrent unit
- glucose forecast
- neural network