Modeling glycemia in humans by means of Grammatical Evolution

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@Article{Hidalgo:2014:ASC,
  author =       "J. Ignacio Hidalgo and J. Manuel Colmenar and 
                 Jose L. Risco-Martin and Alfredo Cuesta-Infante and 
                 Esther Maqueda and Marta Botella and Jose Antonio Rubio",
  title =        "Modeling glycemia in humans by means of Grammatical
                 Evolution",
  journal =      "Applied Soft Computing",
  year =         "2014",
  volume =       "20",
  pages =        "40--53",
  month =        jul,
  keywords =     "genetic algorithms, genetic programming, Grammatical
                 Evolution",
  ISSN =         "1568-4946",
  DOI =          "doi:10.1016/j.asoc.2013.11.006",
  URL =          "http://www.sciencedirect.com/science/article/pii/S156849461300402X",
  size =         "14 pages",
  abstract =     "Diabetes mellitus is a disease that affects to
                 hundreds of millions of people worldwide. Maintaining a
                 good control of the disease is critical to avoid severe
                 long-term complications. In recent years, several
                 artificial pancreas systems have been proposed and
                 developed, which are increasingly advanced. However
                 there is still a lot of research to do. One of the main
                 problems that arises in the (semi) automatic control of
                 diabetes, is to get a model explaining how glycemia
                 (glucose levels in blood) varies with insulin, food
                 intakes and other factors, fitting the characteristics
                 of each individual or patient. This paper proposes the
                 application of evolutionary computation techniques to
                 obtain customised models of patients, unlike most of
                 previous approaches which obtain averaged models. The
                 proposal is based on a kind of genetic programming
                 based on grammars known as Grammatical Evolution (GE).
                 The proposal has been tested with in silico patient
                 data and results are clearly positive. We present also
                 a study of four different grammars and five objective
                 functions. In the test phase the models characterised
                 the glucose with a mean percentage average error of
                 13.69percent, modelling well also both hyper and
                 hypoglycemic situations.",
}

Genetic Programming entries for Jose Ignacio Hidalgo Perez J Manuel Colmenar Jose L Risco-Martin Alfredo Cuesta-Infante Esther Maqueda Marta Botella Jose Antonio Rubio

Citations