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Accession Number ADA571031
Title Predicting Human Subcutaneous Glucose Concentration in Real Time: A Universal Data-Driven Approach.
Publication Date Sep 2011
Media Count 5p
Personal Author J. Reifman R. A. Vigersky S. Rajaraman W. K. Ward Y. Lu
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 approximately 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.
Keywords Cgm(Continuous glucose monitoring)
Feasibility studies
Kalman filtering
Mathematical models
Mathematical prediction
Offline systems
Subcutaneous tissue
Warning systems

Source Agency Non Paid ADAS
NTIS Subject Category 57B - Biochemistry
57D - Clinical Chemistry
Corporate Author Army Medical Research and Materiel Command (Provisional), Fort Detrick, MD. Telemedicine and Advanced Tech Research Center.
Document Type Technical report
Title Note Conference paper.
NTIS Issue Number 1315
Contract Number N/A

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