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Prediction of Glycemia Based on Diabetes Self-Monitoring Data

Marián Tárník(1), Vladimír Bátora(2), Tomáš Ludwig(3), Ivan Ottinger(4*), Eva Miklovičová(5), Ján Murgaš(6)

(1) Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava., Slovakia
(2) Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava., Slovakia
(3) Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava., Slovakia
(4) Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava., Slovakia
(5) Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava., Slovakia
(6) Institute of Robotics and Cybernetics, Faculty of Electrical Engineering and Information Technology, Slovak University of Technology in Bratislava., Slovakia
(*) Corresponding author


DOI: https://doi.org/10.15866/ireaco.v8i2.5232

Abstract


This paper deals with the application of self-monitoring diabetes data that are supplemented by the continuous glucose monitoring for the blood glucose concentration prediction. The short-term predictor is designed and evaluated on three different datasets. A diabetes-specific metrics is used to evaluate the predictors. Standard Least Squares identification as well as an alternative identification method with constraints is considered.
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Keywords


Diabetes; Glucose; Predictors; Least Squares Identification

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References


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