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.
CITATION STYLE
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 Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST (Vol. 166, pp. 166–178). Springer Verlag. https://doi.org/10.1007/978-3-319-33681-7_14
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