Abstract
This paper examines the use of partial least squares regression to predict glycemic variability in subjects with Type I Diabetes Mellitus using measurements from continuous glucose monitoring devices and consumer-grade activity monitoring devices. It illustrates a methodology for generating automated predictions from current and historical data and shows that activity monitoring can improve prediction accuracy substantially.
This work has been supported, in part, JDRF grant number 17-2013-473, NSF grants CCF-1539586, CNS-0905237, CNS-1218808, and ACI-0751315, and NIH grant 1R01EB014877-01. We also acknowledge Wendy C. Bevier and Paige K. Bradley for the William Sansum Diabetes Center for their indispensable contributions.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Krintz, C., Wolski, R., Pinsker, J.E., Dimopoulos, S., Brevik, J., Dassau, E. (2016). On the Use of Consumer-Grade Activity Monitoring Devices to Improve Predictions of Glycemic Variability. In: Leon-Garcia, A., et al. Smart City 360°. SmartCity 360 SmartCity 360 2016 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 166. Springer, Cham. https://doi.org/10.1007/978-3-319-33681-7_14
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DOI: https://doi.org/10.1007/978-3-319-33681-7_14
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