Abstract
Patients suffering from Diabetes Mellitus illness need to control their levels of sugar by a restricted diet, a healthy life and in the cases of those patients that do not produce insulin (or with a severe defect on the action of the insulin they produce), by injecting synthetic insulin before and after the meals. The amount of insulin, namely bolus, to be injected is usually estimated based on the experience of the doctor and of the own patient. During the last years, several computational tools have been designed to suggest the boluses for each patient. Some of the successful approaches to solve this problem are based on obtaining a model of the glucose levels which is then applied to estimate the most appropriate dose of insulin. In this paper we describe some advances in the application of evolutionary computation to obtain those models. In particular, we extend some previous works with Grammatical Evolution, a branch of Genetic Programming. We present results for ten real patients on the prediction on several time horizons. We obtain reliable and individualized predictive models of the glucose regulatory system, eliminating restrictions such as linearity or limitation on the input parameters.
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References
El-Khatib, F.H., Russell, S.J., Nathan, D.M., Sutherlin, R.G., Damiano, E.R.: A bihormonal closed-loop artificial pancreas for type 1 diabetes. Sci Transl Med 2(27), 27ra27–27ra27 (2010)
Colmenar, J.M., Winkler, S.M., Kronberger, G., Maqueda, E., Botella, M., Hidalgo, J.I.: Predicting glycemia in diabetic patients by evolutionary computation and continuous glucose monitoring. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, pp. 1393–1400. ACM (2016)
O’Neill, M., Ryan, C.: Grammatical evolution. IEEE Trans. Evol. Comput. 5(4), 349–358 (2001)
O’Neill, M., Ryan, C.: Grammatical Evolution: Evolutionary Automatic Programming in an Arbitrary Language. Kluwer Academic Publishers, Dordrecht (2003)
Eiben, A.E., Smith, J.E.: Introduction to Evolutionary Computing. Springer, Heidelberg (2003)
Hidalgo, J.I., Colmenar, J.M., Risco-Martin, J.L., Cuesta-Infante, A., Maqueda, E., Botella, M., Rubio, J.A.: Modeling glycemia in humans by means of grammatical evolution. Appl. Soft Comput. 20, 40–53 (2014). Hybrid intelligent methods for health technologies
Colmenar, J.M., Winkler, S.M., Kronberger, G., Maqueda, E., Botella, M., Hidalgo, J.I.: Predicting glycemia in diabetic patients by evolutionary computation and continuous glucose monitoring. In: Proceedings of the 2016 on Genetic and Evolutionary Computation Conference Companion, GECCO 2016 Companion, pp. 1393–1400. ACM, New York (2016)
Adaptive and Bioinspired Systems Group. ABSys JECO (Java Evolutionary COmputation) library (2015). https://github.com/ABSysGroup/jeco
Colmenar, J.M., Hidalgo, J.I., Lanchares, J., Garnica, O., Risco, J.-L., Contreras, I., Sánchez, A., Velasco, J.M.: Compilable phenotypes: speeding-up the evaluation of glucose models in grammatical evolution. In: Squillero, G., Burelli, P. (eds.) EvoApplications 2016. LNCS, vol. 9598, pp. 118–133. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-31153-1_9
Clarke, W., Cox, D., Gonder-Frederick, L., Carter, W., Pohl, S.: Evaluating clinical accuracy of systems for self-monitoring of blood glucose. Diab. Care 10(5), 622–628 (1987)
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Hidalgo, J.I., Colmenar, J.M., Kronberger, G., Winkler, S.M. (2018). Glucose Prognosis by Grammatical Evolution. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds) Computer Aided Systems Theory – EUROCAST 2017. EUROCAST 2017. Lecture Notes in Computer Science(), vol 10671. Springer, Cham. https://doi.org/10.1007/978-3-319-74718-7_55
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DOI: https://doi.org/10.1007/978-3-319-74718-7_55
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