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A genetic algorithm approach to customizing a glucose model based on usual therapeutic parameters

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Abstract

Type 1 diabetes mellitus is a chronic disease characterized by the increase of glucose in the blood due to a defect in the action or in the production of insulin. For completely autonomous glycemic regulation, a model would be required which permits the future evolution of blood glucose to be estimated. One of the main problems in identifying models is the high variability of glucose profiles both from one patient to another, and in the same patient under not very different conditions. In this paper, we propose a method using an evolutionary algorithm to define the values of the parameters of a minimal model based on standard clinical therapy for a several-day horizon. The algorithm is able to show the trend of blood glucose in a 5-day profile by adjusting the glucose model.

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Acknowledgements

This work was funded by the TIN2014-54806-R, TIN2014-57028-R and MTM2013-41765-P. The authors would also like to thank María-Aranzazu Aramendi-Zurimendi and Remedios Martínez-Rodríguez from the Endocrinology and Nutrition Service at the Príncipe de Asturias hospital (Alcalá de Henares, Madrid, Spain). Abbot has supported this research with the donation of a glucose sensor (Free Style Libre).

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Authors and Affiliations

  1. Facultad de Informática, Universidad Complutense de Madrid, Calle del Prof. José García Santesmases, 9, 28040, Madrid, Spain

    Carlos Cervigón & J. Ignacio Hidalgo

  2. Hospital Universitario Príncipe de Asturias, Alcalá de Henares, Madrid, Spain

    Marta Botella

  3. Instituto de Matemática Multidisciplinar, Universitat Politécnica de Valencia, Valencia, Spain

    Rafael-J. Villanueva

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  1. Carlos Cervigón
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  2. J. Ignacio Hidalgo
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  3. Marta Botella
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  4. Rafael-J. Villanueva
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Correspondence to J. Ignacio Hidalgo.

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Cervigón, C., Hidalgo, J.I., Botella, M. et al. A genetic algorithm approach to customizing a glucose model based on usual therapeutic parameters. Prog Artif Intell 6, 255–261 (2017). https://doi.org/10.1007/s13748-017-0121-9

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  • DOI: https://doi.org/10.1007/s13748-017-0121-9

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