Abstract
In this paper detailed analysis of the Hovorka model has been provided. The model describes the dynamics of glucose concentration in case of patients with type 1 diabetes mellitus. The Hovorka model is widely used as a virtual environment and also as a part of controller (so-called an internal model). Due to the popularity of the Hovorka model, its detailed analysis can be helpful in choosing the control algorithm or in simplifying the implementation. The aim was to assess how changes from their base value will affect the glucose output. Results for 3 parameters of the model: rate of an insulin elimination from a patient plasma, endogenous glucose production and total glucose fluctuations independent of insulin were compared. Another purpose of the research was to assess the model nonlinearity intensity. The study was performed on 6 patients who represent the virtual population of type 1 diabetic patients.
The performed analysis indicated that an insulin-glucose system described by the Hovorka model was weakly nonlinear. The values of the nonlinear coefficient were inter-patients varied and depended on an insulin dose. These values ranged: 0.06–10.84. The measured glucose concentration became sensitive to all studied parameters of the Hovorka model. The most sensibilized parameter were glucose fluctuations independent of insulin.
These results of the analysis may be used to develop new control algorithms based on the internal patient model. They will be able to adapt their parameters to the individual patient by updating specific value in each step of the algorithms.
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Acknowledgement
The authors thank Dr. Malgorzata Wilinska and Prof. Roman Hovorka from Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge for their kind and advices during model implementation.
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Radomski, D., Głowacka, J. (2019). Sensitivity Analysis of the Insulin-Glucose Mathematical Model. In: Pietka, E., Badura, P., Kawa, J., Wieclawek, W. (eds) Information Technology in Biomedicine. ITIB 2018. Advances in Intelligent Systems and Computing, vol 762. Springer, Cham. https://doi.org/10.1007/978-3-319-91211-0_40
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