Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models
dc.contributor.author
dc.date.accessioned
2018-10-02T15:39:18Z
dc.date.available
2018-10-02T15:39:18Z
dc.date.issued
2017-11-07
dc.identifier.uri
dc.description.abstract
The large patient variability in human physiology and the effects of variables such as exercise or meals challenge current prediction modeling techniques. Physiological models are very precise but they are typically complex and specific physiological knowledge is required. In contrast, data-based models allow the incorporation of additional inputs and accurately capture the relationship between these inputs and the outcome, but at the cost of losing the physiological meaning of the model. In this work, we designed a hybrid approach comprising physiological models for insulin and grammatical evolution, taking into account the clinical harm caused by deviations from the target blood glucose by using a penalizing fitness function based on the Clarke error grid. The prediction models were built using data obtained over 14 days for 100 virtual patients generated by the UVA/Padova T1D simulator. Midterm blood glucose was predicted for the 100 virtual patients using personalized models and different scenarios. The results obtained were promising; an average of 98.31% of the predictions fell in zones A and B of the Clarke error grid. Midterm predictions using personalized models are feasible when the configuration of grammatical evolution explored in this study is used. The study of new alternative models is important to move forward in the development of alarm-and-control applications for the management of type 1 diabetes and the customization of the patient’s treatments. The hybrid approach can be adapted to predict short-term blood glucose values to detect continuous glucose-monitoring sensor errors and to estimate blood glucose values when the continuous glucose-monitoring system fails to provide them
dc.description.sponsorship
This work was partly supported by the Spanish Ministry of Science and Innovation (grants DPI 2013-46982-C2-2-R and DPI2016-78831-C2-2-R), the People program (Marie Sklodowska-Curie Actions) of the European Union Seventh Framework Programme (FP7/2007-2013) with
agreement (No. 600388) (TECNIOspring programme) of the REA and the Agencia per a la Competitivitat de L’Empresa (ACCIO´), and by the Spanish Government through contract ES-2014-068289
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
Public Library of Science (PLoS)
dc.relation
DPI2013‐46982‐C2‐2‐R
DPI2016-78831-C2-2-R
dc.relation.isformatof
Reproducció digital del document publicat a: https://doi.org/10.1371/journal.pone.0187754
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PLoS ONE, 2017, vol. 12, núm. 11, p. e0187754
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Articles publicats (D-EEEiA)
dc.rights
Attribution 4.0 International
dc.rights.uri
dc.subject
dc.title
Personalized blood glucose prediction: A hybrid approach using grammatical evolution and physiological models
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.relation.projectID
info:eu-repo/grantAgreement/MINECO//DPI2013-46982-C2-2-R/ES/NUEVOS METODOS PARA LA EFICIENCIA Y SEGURIDAD DEL PANCREAS ARTIFICIAL DOMICILIARIO EN DIABETES TIPO 1/
info:eu-repo/grantAgreement/MINECO/Plan Estatal de Investigación Científica y Técnica y de Innovación 2013-2016/DPI2016-78831-C2-2-R/ES/Soluciones para la Mejora de la Eficiencia y Seguridad del Páncreas Artificial mediante Arquitecturas de Control Multivariable Tolerantes a Fallos/
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
027857
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
dc.relation.FundingProgramme
dc.relation.ProjectAcronym
dc.identifier.eissn
1932-6203