Data Based Prediction of Blood Glucose Concentrations Using Evolutionary Methods
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @Article{Hidalgo2017b,
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author = "J. Ignacio Hidalgo and J. Manuel Colmenar and
Gabriel Kronberger and Stephan M. Winkler and Oscar Garnica and
Juan Lanchares",
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title = "Data Based Prediction of Blood Glucose Concentrations
Using Evolutionary Methods",
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journal = "Journal of Medical Systems",
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year = "2017",
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volume = "41",
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number = "9",
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pages = "142",
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month = sep,
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note = "Special issue on Patient Facing Systems",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, Diabetes, Glucose prediction, Continuous
glucose monitoring, Evolutionary computation",
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ISSN = "1573-689X",
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DOI = "doi:10.1007/s10916-017-0788-2",
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size = "20 pages",
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abstract = "Predicting glucose values on the basis of insulin and
food intakes is a difficult task that people with
diabetes need to do daily. This is necessary as it is
important to maintain glucose levels at appropriate
values to avoid not only short-term, but also long-term
complications of the illness. Artificial intelligence
in general and machine learning techniques in
particular have already lead to promising results in
modelling and predicting glucose concentrations. In
this work, several machine learning techniques are used
for the modeling and prediction of glucose
concentrations using as inputs the values measured by a
continuous monitoring glucose system as well as also
previous and estimated future carbohydrate intakes and
insulin injections. In particular, we use the following
four techniques: genetic programming, random forests,
k-nearest neighbours, and grammatical evolution. We
propose two new enhanced modeling algorithms for
glucose prediction, namely (i) a variant of grammatical
evolution which uses an optimized grammar, and (ii) a
variant of tree-based genetic programming which uses a
three-compartment model for carbohydrate and insulin
dynamics. The predictors were trained and tested using
data of ten patients from a public hospital in Spain.
We analyse our experimental results using the Clarke
error grid metric and see that 90percent of the
forecasts are correct (i.e., Clarke error categories A
and B), but still even the best methods produce 5 to
10percent of serious errors (category D) and
approximately 0.5percent of very serious errors
(category E). We also propose an enhanced genetic
programming algorithm that incorporates a
three-compartment model into symbolic regression models
to create smoothed time series of the original
carbohydrate and insulin time series.",
- }
Genetic Programming entries for
Jose Ignacio Hidalgo Perez
J Manuel Colmenar
Gabriel Kronberger
Stephan M Winkler
Oscar Garnica
J Lanchares
Citations