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

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

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Nombre: Cervigón;Hidalgo; ...
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dc.contributor.author Cervigón, Carlos es_ES
dc.contributor.author Hidalgo, J.I. es_ES
dc.contributor.author Botella, M. es_ES
dc.contributor.author Villanueva Micó, Rafael Jacinto es_ES
dc.date.accessioned 2020-07-25T03:31:07Z
dc.date.available 2020-07-25T03:31:07Z
dc.date.issued 2017-09 es_ES
dc.identifier.issn 2192-6352 es_ES
dc.identifier.uri http://hdl.handle.net/10251/148678
dc.description.abstract [EN] 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. es_ES
dc.description.sponsorship This work was funded by the TIN2014-54806-R, TIN2014-57028-R and MTM2013-41765-P. The authors would also like to thank Maria-Aranzazu Aramendi-Zurimendi and Remedios Martinez-Rodriguez from the Endocrinology and Nutrition Service at the Principe de Asturias hospital (Alcala de Henares, Madrid, Spain). Abbot has supported this research with the donation of a glucose sensor (Free Style Libre). es_ES
dc.language Inglés es_ES
dc.publisher Springer-Verlag es_ES
dc.relation.ispartof Progress in Artificial Intelligence es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Genetic algorithm es_ES
dc.subject Evolutionary computation es_ES
dc.subject Diabetes es_ES
dc.subject Minimal model es_ES
dc.subject.classification MATEMATICA APLICADA es_ES
dc.title A genetic algorithm approach to customizing a glucose model based on usual therapeutic parameters es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1007/s13748-017-0121-9 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2014-57028-R/ES/DESARROLLLO COLABORATIVO DE SOLUCIONES AAL/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//TIN2014-54806-R/ES/DESARROLLO DE SISTEMAS ADAPTATIVOS Y BIOINSPIRADOS PARA EL CONTROL GLUCEMICO CON INFUSORES SUBCUTANEOS CONTINUOS DE INSULINA Y MONITORES CONTINUOS DE GLUCOSA./ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//MTM2013-41765-P/ES/METODOS COMPUTACIONALES PARA ECUACIONES DIFERENCIALES ALEATORIAS: TEORIA Y APLICACIONES/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Matemática Aplicada - Departament de Matemàtica Aplicada es_ES
dc.description.bibliographicCitation Cervigón, C.; Hidalgo, J.; Botella, M.; Villanueva Micó, RJ. (2017). A genetic algorithm approach to customizing a glucose model based on usual therapeutic parameters. Progress in Artificial Intelligence. 6(3):255-261. https://doi.org/10.1007/s13748-017-0121-9 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1007/s13748-017-0121-9 es_ES
dc.description.upvformatpinicio 255 es_ES
dc.description.upvformatpfin 261 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 6 es_ES
dc.description.issue 3 es_ES
dc.relation.pasarela S\338370 es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.description.references Acedo, L., Moraño, J.-A., Santonja, F.-J., Villanueva, R-J.: A deterministic model for highly contagious diseases: the case of varicella. Phys. A Stat. Mech. Appl. 450, 278–286 (2016). ISSN 0378-4371. doi: 10.1016/j.physa.2015.12.153 es_ES
dc.description.references Eshelman, L.J., Schaffer, J.D.: Real-coded genetic algorithms and interval-schemata. Found. Genet. Algorithms 2, 187–202 (1993) es_ES
dc.description.references Ono, I., Kobayashi, S.: A real-coded genetic algorithm for function optimization using unimodal normal distribution crossover. In: Back, T. (ed.) Proceedings of the Seventh International Conference on Genetic Algorithms, pp. 246–253. Morgan Kaufmann, San Mateo (1997) es_ES
dc.description.references Janikow, C.Z., Michalewicz, Z.: An experimental comparison of binary and floating point representations in genetic algorithms. In: ICGA, pp. 31–36 (1991) es_ES
dc.description.references Goldberg, D.E.: Real coded genetic algorithms virtual alphabets and blocking. Complex Syst. 5, 139–167 (1991) es_ES
dc.description.references Corcoran, A.L., Sen S.: Using real-valued genetic algorithms to evolve sets for classification. In: IEEE Conference on Evolutionary Computation, pp. 120–124 (1994) es_ES
dc.description.references Deb, K., Agarwal, R.: Simulated binary crossover for continuous search space. Complex Syst. 9, 115–148 (1995) es_ES
dc.description.references Tsutsui, S., Yamamura, M., Higuchi, T.: Multi-parent recombination with simplex crossover in real coded genetic algorithms. In: Proceedings of the 1st Annual Conference on Genetic and Evolutionary Computation, vol. 1, pp. 657–664. Morgan Kaufmann Publishers Inc. (1999) es_ES
dc.description.references Back, T., Schwefel, H.-P.: An overview of evolutionary algorithms for parameter optimization. Evol. Comput. 1(1), 1–23 (1993) es_ES
dc.description.references Prud’homme, T., Bock, A., François, G., Gillet, D.: Preclinically assessed optimal control of postprandial glucose excursions for type 1 patients with diabetes. In: IEEE Conference on Automation Science and Engineering (CASE) (2011). doi: 10.1109/CASE.2011.6042510 es_ES
dc.description.references Nelder, J.A., Mead, R.: A simplex method for function minimization. Comput. J. 7, 308–313 (1964). doi: 10.1093/comjnl/7.2.155 es_ES
dc.description.references Herrera, F., Lozano, M., Sánchez, A.M.: A taxonomy for the crossover operator for real coded genetic algorithms: an experimental study. Int. J. Intell. Syst. 18, 309–338 (2003) es_ES
dc.description.references Deb, K., Anand, A., Joshi, D.: A computationally efficient evolutionary algorithm for real-parameter evolution. Evol. Comput. 10(4), 371–395 (2002) es_ES
dc.description.references Davis, L.: Handbook on Genetic Algorithm. Van Nostral Reinhol, New York (1991) es_ES
dc.description.references Yang, J-M., Kao, C.Y.: A combined evolutionary algorithm for real parameter optimization. In: Proceedings of 1996 IEEE International Conference on Evolutionary Computation, pp. 732–737 . IEEE Service Center, Piscataway (1996) es_ES
dc.description.references Surry, P.D., Radcliffe, N.J.: Real representations. In: Belew, R.K., Vose, M.D. (eds.) Foundations of Genetic Algorithms 4, pp. 343–363. Morgan Kaufmann Publishers, San Francisco (1996) es_ES
dc.description.references Syswerda, G.: Uniform crossover in genetic algorithms. In: Schaffer, J.D. (ed.) Proceedings of the Third International Conference on Genetic Algorithms, pp. 2–9. Morgan Kaufmann Publishers, San Mateo (1989) es_ES
dc.description.references Araujo, L., Cervigón, C.: Evolutionary algorithms: a practical approach. RAMA, pp.  111 (2009) es_ES
dc.description.references Michalewicz, Z.: Genetic Algorithms $$+$$ + Data Structures $$=$$ = Evolution Programs, 3rd edn. Springer, New York (1996) es_ES


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