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

Created by W.Langdon from gp-bibliography.bib Revision:1.8081

@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-Serrano Jose Ignacio Hidalgo Perez

Citations