Glucose forecasting using genetic programming and latent glucose variability features
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gp-bibliography.bib Revision:1.8081
- @Article{CONTADOR:2021:ASC,
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author = "Sergio Contador and J. Manuel Velasco and
Oscar Garnica and J. Ignacio Hidalgo",
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title = "Glucose forecasting using genetic programming and
latent glucose variability features",
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journal = "Applied Soft Computing",
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volume = "110",
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pages = "107609",
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year = "2021",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2021.107609",
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URL = "https://www.sciencedirect.com/science/article/pii/S1568494621005305",
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keywords = "genetic algorithms, genetic programming, Diabetes,
Continuous glucose monitoring, Glucose variability",
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abstract = "This paper investigates a set of genetic programming
methods to obtain accurate predictions of subcutaneous
glucose values from diabetic patients. We explore the
usefulness of different features that identify the
latent glucose variability. New features, including
average glucose, glucose variability and glycemic risk,
are generated as input variables of the genetic
programming algorithm in order to improve the accuracy
of the models in the prediction phase. The performance
of traditional genetic programming, and models created
with classified glucose values, are compared to those
using latent glucose variability features. We
experimented with a set of 18 different features and we
also performed a study of the importance of the
variables in the models. The Bayesian statistical
analysis shows that the use of glucose variability as
latent variables improved the predictions of the
models, not only in terms of RMSE, but also in the
reduction of dangerous predictions, i.e., those
predictions that could lead to wrong decisions in the
clinical practice",
- }
Genetic Programming entries for
Sergio Contador
Jose Manuel Velasco Cabo
Oscar Garnica
Jose Ignacio Hidalgo Perez
Citations