Predicting Glycemia in Diabetic Patients By Evolutionary Computation and Continuous Glucose Monitoring

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@InProceedings{Colmenar:2016:GECCOcomp,
  author =       "J. Manuel Colmenar and Stephan M. Winkler and 
                 Gabriel Kronberger and Esther Maqueda and Marta Botella and 
                 J. Ignacio Hidalgo",
  title =        "Predicting Glycemia in Diabetic Patients By
                 Evolutionary Computation and Continuous Glucose
                 Monitoring",
  booktitle =    "GECCO '16 Companion: Proceedings of the Companion
                 Publication of the 2016 Annual Conference on Genetic
                 and Evolutionary Computation",
  year =         "2016",
  editor =       "Tobias Friedrich and Frank Neumann and 
                 Andrew M. Sutton and Martin Middendorf and Xiaodong Li and 
                 Emma Hart and Mengjie Zhang and Youhei Akimoto and 
                 Peter A. N. Bosman and Terry Soule and Risto Miikkulainen and 
                 Daniele Loiacono and Julian Togelius and 
                 Manuel Lopez-Ibanez and Holger Hoos and Julia Handl and 
                 Faustino Gomez and Carlos M. Fonseca and 
                 Heike Trautmann and Alberto Moraglio and William F. Punch and 
                 Krzysztof Krawiec and Zdenek Vasicek and 
                 Thomas Jansen and Jim Smith and Simone Ludwig and JJ Merelo and 
                 Boris Naujoks and Enrique Alba and Gabriela Ochoa and 
                 Simon Poulding and Dirk Sudholt and Timo Koetzing",
  isbn13 =       "978-1-4503-4323-7",
  pages =        "1393--1400",
  address =      "Denver, Colorado, USA",
  month =        "20-24 " # jul,
  keywords =     "genetic algorithms, genetic programming, grammatical
                 evolution",
  organisation = "SIGEVO",
  DOI =          "doi:10.1145/2908961.2931734",
  publisher =    "ACM",
  publisher_address = "New York, NY, USA",
  abstract =     "Diabetes mellitus is a disease that affects more than
                 three hundreds million people worldwide. Maintaining a
                 good control of the disease is critical to avoid not
                 only severe long-term complications but also dangerous
                 short-term situations. Diabetics need to decide the
                 appropriate insulin injection, thus they need to be
                 able to estimate the level of glucose they are going to
                 have after a meal. In this paper we use machine
                 learning techniques for predicting glycemia in diabetic
                 patients. The algorithms use data collected from real
                 patients by a continuous glucose monitoring system, the
                 estimated number of carbohydrates, and insulin
                 administration for each meal. We compare (1) non-linear
                 regression with fixed model structure, (2)
                 identification of prognosis models by symbolic
                 regression using genetic programming, (3) prognosis by
                 k-nearest-neighbour time series search, and (4)
                 identification of prediction models by grammatical
                 evolution. We consider predictions horizons of 30, 60,
                 90 and 120 minutes.",
  notes =        "Distributed at GECCO-2016.",
}

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

Citations