Examining Sensor Agreement in Neural Network Blood Glucose Prediction

Research output: Contribution to journalArticlepeer-review

Abstract

Successful measurements of interstitial glucose are a key component in providing effective care for patients with diabetes. Recently, there has been significant interest in using neural networks to forecast future glucose values from interstitial measurements collected by continuous glucose monitors (CGMs). While prediction accuracy continues to improve, in this work we investigated the effect of physiological sensor location on neural network blood glucose forecasting. We used clinical data from patients with Type 2 Diabetes who wore blinded FreeStyle Libre Pro CGMs (Abbott) on both their right and left arms continuously for 12 weeks. We trained patient-specific prediction algorithms to test the effect of sensor location on neural network forecasting (N = 13, Female = 6, Male = 7). In 10 of our 13 patients, we found at least one significant (P < .05) increase in forecasting error in algorithms which were tested with data taken from a different location than data which was used for training. These reported results were independent from other noticeable physiological differences between subjects (eg, height, age, weight, blood pressure) and independent from overall variance in the data. From these results we observe that CGM location can play a consequential role in neural network glucose prediction.
Original languageEnglish (US)
JournalJournal of Diabetes Science and Technology
Early online dateJun 10 2021
DOIs
StatePublished - 2021

Bibliographical note

Funding Information:
This work was made possible by the Academic Health Center and the Earl E. Bakken Medical Devices Center.

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 University of Minnesota Academic Health Center (AHC-FRD-17-08 to LSC) and the National Institutes of Health (NIH National Center for Advancing Translational Sciences, UL1TR002494). 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:
© 2021 Diabetes Technology Society.

Keywords

  • blood glucose prediction
  • continuous glucose monitoring
  • neural network

PubMed: MeSH publication types

  • Journal Article

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