Evolving Classification Rules for Predicting Hypoglycemia Events
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InProceedings{De-La-Cruz:2022:CEC,
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author = "Marina {De La Cruz Lopez} and Carlos Cervigon and
Jorge Alvarado and Marta Botella-Serrano and
J. Ignacio Hidalgo",
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title = "Evolving Classification Rules for Predicting
Hypoglycemia Events",
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booktitle = "2022 IEEE Congress on Evolutionary Computation (CEC)",
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year = "2022",
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editor = "Carlos A. Coello Coello and Sanaz Mostaghim",
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address = "Padua, Italy",
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month = "18-23 " # jul,
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keywords = "genetic algorithms, genetic programming, Grammatical
Evolution, Structured Grammatical Evolution, Wearable
Health Monitoring Systems, Predictive models,
Prediction algorithms, Diabetes, Glucose, Grammar,
Proposals, Diabetes, Hypoglycemia prediction, PWD, Rule
System",
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isbn13 = "978-1-6654-6708-7",
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URL = "https://human-competitive.org/sites/default/files/humies_hidalgo.txt",
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URL = "https://human-competitive.org/sites/default/files/postable_version_2022_wccic_marina.pdf",
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DOI = "doi:10.1109/CEC55065.2022.9870380",
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size = "8 pages",
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abstract = "People with diabetes have to properly manage their
blood glucose levels in order to avoid acute
complications. This is a difficult task and an accurate
and timely prediction may be of vital importance,
specially of extreme values. Perhaps one of the main
concerns of people with diabetes is to suffer an
hypoglycemia (low value) event and moreover, that the
event will be prolonged in time. It is crucial to
predict events of hyperglycemia (high value) and
hypoglycemia that may cause health damages in the short
term and potential permanent damages in the long term.
The aim of this paper is to describe our research on
predicting hypoglycemia events using Dynamic structured
Grammatical Evolution. Our proposal gives white box
models induced by a grammar based on if-then-else
conditions. We trained and tested our system with real
data collected from 5 different diabetic patients,
producing 30 minutes predictions with encouraging
results.",
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notes = "1st author given as Marina De La Cruz, Universidad
Complutense de Madrid, Madrid, Spain
marcru06@ucm.es
Finalist 2023 HUMIES
Also known as \cite{9870380}
Predict human blood glucose concentration using number
of steps smartwatch => energy expended by person and
wearable mobile Continuous Glucose Monitoring System",
- }
Genetic Programming entries for
Marina Miguel De la Cruz Lopez
Carlos Cervigon Ruckauer
Jorge Alvarado
Marta Botella-Serrano
Jose Ignacio Hidalgo Perez
Citations