Combining data augmentation, EDAs and grammatical evolution for blood glucose forecasting
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gp-bibliography.bib Revision:1.7964
- @Article{velasco:2018:MC,
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author = "Jose Manuel Velasco and Oscar Garnica and
Juan Lanchares and Marta Botella and J. Ignacio Hidalgo",
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title = "Combining data augmentation, {EDAs} and grammatical
evolution for blood glucose forecasting",
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journal = "Memetic Computing",
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year = "2018",
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volume = "10",
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number = "3",
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pages = "267--277",
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keywords = "genetic algorithms, genetic programming, grammatical
evolution, Diabetes, Time series forecasting, Data
augmentation",
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URL = "https://rdcu.be/cz6rt",
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URL = "http://link.springer.com/article/10.1007/s12293-018-0265-6",
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DOI = "doi:10.1007/s12293-018-0265-6",
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size = "11 pages",
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abstract = "The ideal solution for diabetes mellitus type 1
patients is the generalization of artificial pancreas
systems. Artificial pancreas will control blood glucose
levels of diabetics, improving their quality of live.
At the core of the system, an algorithm will forecast
future glucose levels as a function of food ingestion
and insulin bolus sizes. In previous works several
evolutionary computation techniques has been proposed
as modeling or identification techniques in this area.
One of the main obstacles that researchers have found
for training the models is the lack of significant
amounts of data. As in many other fields in medicine,
the collection of data from real patients is not an
easy task, since it is necessary to control the
environmental and patient conditions. In this paper, we
propose three evolutionary algorithms that generate
synthetic glucose time series using real data from a
patient. This way, the models can be trained with an
augmented data set. The synthetic time series are used
to train grammatical evolution models that work
together in an ensemble. Experimental results show
that, in a scarce data context, grammatical evolution
models can get more accurate and robust predictions
using data augmentation. In particular we reduce the
number of potentially dangerous predictions to 0 for a
30 min horizon, 2.5percent for 60 min, 3.6percent on 90
min and 5.5percent for 2 h. The Ensemble approach
presented in this paper showed excellent performance
when compared to not only a classical approach such as
ARIMA, but also with other grammatical evolution
approaches. We tested our techniques with data from
real patients.",
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notes = "Universidad Complutense, Madrid, Spain",
- }
Genetic Programming entries for
Jose Manuel Velasco Cabo
Oscar Garnica
J Lanchares
Marta Botella-Serrano
Jose Ignacio Hidalgo Perez
Citations