Clarke and parkes error grid analysis of diabetic glucose models obtained with evolutionary computation

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@InProceedings{Hidalgo:2014:GECCOcomp,
  author =       "J. Ignacio Hidalgo and J. Manuel Colmenar and 
                 Jose L. Risco-Martin and Esther Maqueda and Marta Botella and 
                 Jose Antonio Rubio and Alfredo Cuesta-Infante and 
                 Oscar Garnica and Juan Lanchares",
  title =        "Clarke and parkes error grid analysis of diabetic
                 glucose models obtained with evolutionary computation",
  booktitle =    "GECCO 2014 Workshop on Medical Applications of Genetic
                 and Evolutionary Computation (MedGEC)",
  year =         "2014",
  editor =       "Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton",
  isbn13 =       "978-1-4503-2881-4",
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  pages =        "1305--1312",
  month =        "12-16 " # jul,
  organisation = "SIGEVO",
  address =      "Vancouver, BC, Canada",
  URL =          "http://doi.acm.org/10.1145/2598394.2609856",
  DOI =          "doi:10.1145/2598394.2609856",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  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, a lot of
                 research has been made to improve the quality of life
                 of the diabetic patient, especially in the automation
                 of glucose level control. One of the main problems that
                 arises in the (semi) automatic control of diabetes, is
                 to obtain a model that explains the behaviour of blood
                 glucose levels with insulin, food intakes and other
                 external factors, fitting the characteristics of each
                 individual or patient. Recently, Grammatical Evolution
                 (GE), has been proposed to solve this lack of models. A
                 proposal based on GE was able to obtain customised
                 models of five in-silico patient data with a mean
                 percentage average error of 13.69percent, modelling
                 well also both hyper and hypoglycemic situations. In
                 this paper we have extended the study of Error Grid
                 Analysis (EGA) to prediction models in up to 8
                 in-silico patients. EGA is commonly used in
                 Endocrinology to test the clinical significance of
                 differences between measurements and real value of
                 blood glucose, but has not been used before as a metric
                 in obtention of glycemia models.",
  notes =        "Also known as \cite{2609856} Distributed at
                 GECCO-2014.",
}

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

Citations