Data-Based Identification of Prediction Models for Glucose

Created by W.Langdon from gp-bibliography.bib Revision:1.3872

@InProceedings{Velasco:2015:GECCOcomp,
  author =       "J. Manuel Velasco and Stephan Winkler and 
                 J. Ignacio Hidalgo and Oscar Garnica and Juan Lanchares and 
                 J. Manuel Colmenar and Esther Maqueda and 
                 Marta Botella and Jose-Antonio Rubio",
  title =        "Data-Based Identification of Prediction Models for
                 Glucose",
  booktitle =    "GECCO 2015 Medical Applications of Genetic and
                 Evolutionary Computation (MedGEC'15) Workshop",
  year =         "2015",
  editor =       "Stephen L. Smith and Stefano Cagnoni and 
                 Robert M. Patton",
  isbn13 =       "978-1-4503-3488-4",
  keywords =     "genetic algorithms, genetic programming",
  pages =        "1327--1334",
  month =        "11-15 " # jul,
  organisation = "SIGEVO",
  address =      "Madrid, Spain",
  URL =          "http://doi.acm.org/10.1145/2739482.2768508",
  DOI =          "doi:10.1145/2739482.2768508",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Diabetes mellitus is a disease that affects to
                 hundreds of million of people worldwide. Maintaining a
                 good control of the disease is critical to avoid severe
                 long-term complications. One of the main problems that
                 arise in the (semi) automatic control of diabetes, is
                 to get a model explaining how glucose levels in blood
                 vary with insulin, food intakes and other factors,
                 fitting the characteristics of each individual or
                 patient. In this paper we compare genetic programming
                 techniques with a set of classical identification
                 techniques: classical simple exponential smoothing,
                 Holt's smoothing (linear, exponential and damped),
                 classical Holt and Winters methods and auto regressive
                 integrated moving average modelling. We consider
                 predictions horizons of 30, 60, 90 and 120 minutes.
                 Experimental results shows the difficulty of predicting
                 glucose values for more than 60 minutes and the
                 necessity of adapt GP techniques for those dynamic
                 environments.",
  notes =        "Also known as \cite{2768508} Distributed at
                 GECCO-2015.",
}

Genetic Programming entries for Jose Manuel Velasco Cabo Stephan M Winkler Jose Ignacio Hidalgo Perez Oscar Garnica J Lanchares J Manuel Colmenar Esther Maqueda Marta Botella Jose Antonio Rubio

Citations