Data augmentation and evolutionary algorithms to improve the prediction of blood glucose levels in scarcity of training data
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{velasco:2017:CEC,
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author = "Jose Manuel Velasco and Oscar Garnica and
Sergio Contador and Juan Lanchares and Esther Maqueda and
Marta Botella and J. Ignacio Hidalgo",
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booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Data augmentation and evolutionary algorithms to
improve the prediction of blood glucose levels in
scarcity of training data",
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year = "2017",
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editor = "Jose A. Lozano",
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pages = "2193--2200",
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address = "Donostia, San Sebastian, Spain",
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publisher = "IEEE",
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month = "5-8 " # jun,
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, biochemistry, blood, diseases, evolutionary
computation, learning (artificial intelligence),
medical computing, pattern classification, sugar,
artificial pancreas systems, blood glucose control,
blood glucose level prediction, classification system,
data augmentation, data collection, diabetes mellitus
type 1 patients, evolutionary algorithms, grammatical
evolution model, insulin bolus sizes, patient response,
personal factors, scenario selection, training data
scarcity, Data models, Grammar, Insulin, Predictive
models, Time series analysis",
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isbn13 = "978-1-5090-4601-0",
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DOI = "doi:10.1109/CEC.2017.7969570",
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abstract = "Diabetes Mellitus Type 1 patients are waiting for the
arrival of the Artificial Pancreas. Artificial Pancreas
systems will control the blood glucose of patients,
improving their quality of life and reducing the risks
they face daily. At the core of the Artificial
Pancreas, an algorithm will forecast future glucose
levels and estimate insulin bolus sizes. Grammatical
Evolution has been proved as a suitable algorithm for
predicting glucose levels. Nevertheless, one of the
main obstacles that researches have found for training
the Grammatical Evolution models is the lack of
significant amounts of data. As in many other fields in
medicine, the collection of data from real patients is
very complex along with the fact that the patient's
response can vary in a high degree due to a lot of
personal factors which can be seen as different
scenarios. In this paper, we propose both a
classification system for scenario selection and a data
augmentation algorithm that generates synthetic glucose
time series from real data. Our experimental results
show that, in a scarce data context, Grammatical
Evolution models can get more accurate and robust
predictions using scenario selection and data
augmentation.",
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notes = "IEEE Catalog Number: CFP17ICE-ART Also known as
\cite{7969570}",
- }
Genetic Programming entries for
Jose Manuel Velasco Cabo
Oscar Garnica
Sergio Contador
J Lanchares
Esther Maqueda
Marta Botella-Serrano
Jose Ignacio Hidalgo Perez
Citations