| Autor: |
Aaron P. Tucker, Arthur G. Erdman, Pamela J. Schreiner, Sisi Ma, Lisa S. Chow |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Journal of Diabetes Science and Technology. :193229682211008 |
| ISSN: |
1932-2968 |
| Popis: |
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. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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