Clarke and parkes error grid analysis of diabetic glucose models obtained with evolutionary computation
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @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-Serrano
Jose Antonio Rubio
Alfredo Cuesta-Infante
Oscar Garnica
J Lanchares
Citations