Abstract
This study presents a metabolic modeling scheme for glucose prediction of diabetic patients that is intended for use in mobile devices. We investigate the ability to model the multivariate, nonlinear and dynamic interactions in glucose metabolism using free-living data acquired from wearable sensors or inserted through suitable mobile applications. The physiological processes related to diabetes are simulated by compartmental models, which quantify the absorption of subcutaneously administered insulin, the absorption of glucose from the gut following a meal, as well as the effects of exercise on plasma glucose and insulin dynamics. In addition, Support Vector machines for Regression are employed to provide individualized predictions of the subcutaneous glucose concentrations. The proposed scheme is evaluated in terms of its predictive ability using real data recorded from two type 1 diabetic patients. Also, the incorporation of the predictive model in an integrated diabetes monitoring and management system is discussed.
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References
Sparacino, G., Zanderigo, F., Corazza, S., Maran, A., Facchineti, A., Cobelli, C.: Glucose Concentration can be Predicted Ahead in Time from Continuous Glucose Monitoring Sensor Time-Series. IEEE Trans. Biomed. Eng. 54(5), 931–937 (2007)
Gani, A., Gribok, A.V., Lu, Y., Ward, W.K., Vigersky, R.A., Reifman, J.: Universal Glucose Models for Predicting Subcutaneous Glucose Concentration in Humans. IEEE Trans. Inform. Tech. Biomed. 14(1), 157–165 (2010)
Stahl, F., Johansson, R.: Diabetes mellitus modeling and short-term prediction based on blood glucose measurements. Mathematical Biosciences 217, 101–117 (2008)
Rollins, D., Bhandari, N., Kleinedler, J., Kotz, K., Strohbehn, A., Boland, L., Murphy, M., Andre, D., Vyas, N., Welk, G., Franke, W.E.: Free-living inferential modeling of blood glucose level using only noninvasive inputs. J. Process Control. 20(1), 95–107 (2010)
Valleta, J.J., Chipperfield, A.J., Byrne, C.D.: Gaussian process modeling of blood glucose response to free-living physical activity data in people with type 1 diabetes. In: 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, pp. 4913–4916 (2009)
Georga, E.I., Protopappas, V., Guillen, A., Fico, G., Ardigo, D., Arredondo, M., Exarchos, T., Polyzos, D., Fotiadis, D.I.: Data Mining for Blood Glucose Prediction and Knowledge Discovery in Diabetic Patients: The METABO Diabetes Modeling and Management System. In: 31st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Minneapolis, pp. 5633–5636 (2009)
Smola, A.J., Scholkopf, B.: A tutorial on support vector regression. Statistics and Computing 14, 199–222 (2004)
Tarin, C., Teufel, E., Pico, J., Bondia, J., Pfleiderer, H.J.: A Comprehensive Pharmacokinetic Model of Insulin Glargine and Other Insulin Formulations. IEEE Trans. Biomed. Eng. 52(12), 1994–2005 (2005)
Lehmann, E.D., Deutsch, T.: A physiological model of glucose-insulin interaction in Type 1 diabetes mellitus. J. Biomed. Eng. 14, 235–242 (1992)
Roy, A., Parker, R.S.: Dynamic Modeling of Exercise Effects on Plasma Glucose and Insulin Levels. J. Diabetes Sci. Technol. 1(3), 338–347 (2007)
Kovatchev, B.P., Gonder-Frederick, L.A., Cox, D.J., Clarke, W.L.: Evaluating the accuracy of continuous glucose monitoring sensors: Continuous glucose-error grid analysis illustrated by TheraSense Freestyle Navigator data. Diabetes Care 27(8), 1922–1928 (2004)
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© 2011 ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering
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Georga, E.I., Protopappas, V.C., Fotiadis, D.I. (2011). Predictive Metabolic Modeling for Type 1 Diabetes Using Free-Living Data on Mobile Devices. In: Lin, J.C., Nikita, K.S. (eds) Wireless Mobile Communication and Healthcare. MobiHealth 2010. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 55. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-20865-2_24
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DOI: https://doi.org/10.1007/978-3-642-20865-2_24
Publisher Name: Springer, Berlin, Heidelberg
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