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Data-driven Modeling for Diabetes pp 1–35Cite as

Data-Driven and Minimal-Type Compartmental Insulin-Glucose Models: Theory and Applications

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Part of the book series: Lecture Notes in Bioengineering ((LNBE))

Abstract

This chapter initially presents the results of a computational study that compares simulated compartmental and Volterra models of the dynamic effects of insulin on blood glucose concentration in humans. In this context, we employ the general class of Volterra-type models that are estimated from input-output data, and the widely used “minimal model” as well as an augmented form of it, which incorporates the effect of insulin secretion by the pancreas. We demonstrate both the equivalence between the two approaches analytically and the feasibility of obtaining accurate Volterra models from insulin-glucose data generated from the compartmental models. We also present results from applying the proposed approach to quantifying the dynamic interactions between spontaneous insulin and glucose fluctuations in a fasting dog. The results corroborate the proposition that it may be feasible to obtain data-driven models in a more general and realistic operating context, without resorting to the restrictive prior assumptions and simplifications regarding model structure and/or experimental protocols (e.g. glucose tolerance tests) that are necessary for the compartmental models proposed previously. These prior assumptions may lead to results that are improperly constrained or biased by preconceived (and possibly erroneous) notions—a risk that is avoided when we let the data guide the inductive selection of the appropriate model.

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Acknowledgements

This work has been funded by the University of Cyprus internal research grant ”Nonlinear, data-driven modeling and model-based control of blood glucose”. The authors would like to thank Prof. Richard N. Bergman and Dr. Katrin Huecking for providing the experimental data. This work was supported in part by the NIH/NIBIB Center Grant No. P41-EB001978 to the Biomedical Simulations Resource at the University of Southern California.

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Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Cyprus, P.O. Box 20537, Kallipoleos 75, 1678, Nicosia, Cyprus

    Georgios D. Mitsis

  2. Department of Bioengineering, McGill University, P.O. Box 20537, 817 Sherbrooke St. W., Macdonald Engineering Building, Montreal, QC, H3A 0C3, Canada

    Georgios D. Mitsis

  3. Department of Biomedical Engineering, University of Southern California, 1042 Downey Way, Denney Research Center 140, Los Angeles, 90089, CA, USA

    Vasilis Z. Marmarelis

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  1. Georgios D. Mitsis
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  2. Vasilis Z. Marmarelis
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Correspondence to Georgios D. Mitsis .

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Editors and Affiliations

  1. Biomedical Engineering, University of Southern California, Los Angeles, California, USA

    Vasilis Marmarelis

  2. Electrical and Computer Engineering, Greece, University of Cyprus,Nicosia,Greese and McGill University, Montreal QC, Canada

    Georgios Mitsis

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Mitsis, G.D., Marmarelis, V.Z. (2014). Data-Driven and Minimal-Type Compartmental Insulin-Glucose Models: Theory and Applications. In: Marmarelis, V., Mitsis, G. (eds) Data-driven Modeling for Diabetes. Lecture Notes in Bioengineering. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-54464-4_1

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  • DOI: https://doi.org/10.1007/978-3-642-54464-4_1

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