Glucose Prognosis by Grammatical Evolution

Created by W.Langdon from gp-bibliography.bib Revision:1.8051

@InProceedings{DBLP:conf/eurocast/HidalgoCKW17,

• author = "Jose Ignacio Hidalgo and J. Manuel Colmenar and Gabriel Kronberger and Stephan M. Winkler",
• title = "Glucose Prognosis by Grammatical Evolution",
• booktitle = "16th International Conference on Computer Aided Systems Theory, EUROCAST 2017, Part I",
• year = "2017",
• editor = "Roberto Moreno-Diaz and Franz Pichler and Alexis Quesada-Arencibia",
• volume = "10671",
• series = "Lecture Notes in Computer Science",
• pages = "455--463",
• address = "Las Palmas de Gran Canaria, Spain",
• month = feb # " 19-24",
• publisher = "Springer",
• note = "Revised Selected Papers",
• keywords = "genetic algorithms, genetic programming, Grammatical Evolution",
• isbn13 = "978-3-319-74717-0",
• URL = "https://doi.org/10.1007/978-3-319-74718-7_55",
• DOI = "doi:10.1007/978-3-319-74718-7_55",
• timestamp = "Fri, 26 Jan 2018 12:44:51 +0100",
• biburl = "https://dblp.org/rec/bib/conf/eurocast/BurlacuAKKW17",
• bibsource = "dblp computer science bibliography, https://dblp.org",
• size = "9 pages",
• 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.",

}

Genetic Programming entries for Jose Ignacio Hidalgo Perez J Manuel Colmenar Gabriel Kronberger Stephan M Winkler

Citations