NDLI: Predicting human subcutaneous glucose concentration in real time: A universal data-driven approach

Content Provider	IEEE Xplore Digital Library
Author	Yinghui Lu Rajaraman, S. Ward, W.K. Vigersky, R.A. Reifman, J.
Copyright Year	2011
Description	Author affiliation: BIC, TATRC, USAMRMC, ATTN: MCMR-TT, 504 Scott Street, Fort Detrick, MD 21702 (Reifman, J.) \|\| BIC, TATRC, USAMRMC, Fort Detrick, MD 21702 USA (Rajaraman, S.) \|\| Oregon Health and Sciences University (Ward, W.K.) \|\| Bioinformatics Cell (BIC), TATRC, USAMRMC, Fort Detrick, MD 21702 USA (Yinghui Lu) \|\| Diabetes Institute, Walter Reed Army Medical Center, Washington, DC 20307 USA (Vigersky, R.A.)
Abstract	Continuous glucose monitoring (CGM) devices measure and record a patient's subcutaneous glucose concentration as frequently as every minute for up to several days. When coupled with data-driven mathematical models, CGM data can be used for short-term prediction of glucose concentrations in diabetic patients. In this study, we present a real-time implementation of a previously developed offline data-driven algorithm. The implementation consists of a Kalman filter for real-time filtering of CGM data and a data-driven autoregressive model for prediction. Results based on CGM data from 3 different studies involving 34 type 1 and 2 diabetic patients suggest that the proposed real-time approach can yield ∼10-min-ahead predictions with clinically acceptable accuracy and, hence, could be useful as a tool for warning against impending glucose deregulation episodes. The results further support the feasibility of “universal” glucose prediction models, where an offline-developed model based on one individual's data can be used to predict the glucose levels of any other individual in real time.
Starting Page	7945
Ending Page	7948
File Size	515781
Page Count	4
File Format	PDF
ISBN	9781424441211
ISSN	1557170X
e-ISBN	9781457715891
e-ISBN	9781424441228
DOI	10.1109/IEMBS.2011.6091959
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-08-30
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data models Real time systems Predictive models Sugar Kalman filters Diabetes Training data
Content Type	Text
Resource Type	Article
Subject	Signal Processing Biomedical Engineering Health Informatics Computer Vision and Pattern Recognition

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