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Individual Categorisation of Glucose Profiles Using Compositional Data Analysis

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Individual Categorisation of Glucose Profiles Using Compositional Data Analysis

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Nombre: Biagi;Bertachi;Giménez ...
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dc.contributor.author Biagi, L. es_ES
dc.contributor.author Bertachi, A. es_ES
dc.contributor.author Giménez, M. es_ES
dc.contributor.author Conget, I. es_ES
dc.contributor.author Bondía Company, Jorge es_ES
dc.contributor.author Martín-Fernández, Josep Antoni es_ES
dc.contributor.author Vehí, Josep es_ES
dc.date.accessioned 2020-07-10T03:32:04Z
dc.date.available 2020-07-10T03:32:04Z
dc.date.issued 2019-12-01 es_ES
dc.identifier.issn 0962-2802 es_ES
dc.identifier.uri http://hdl.handle.net/10251/147749
dc.description.abstract [EN] The aim of this study was to apply a methodology based on compositional data analysis (CoDA) to categorise glucose profiles obtained from continuous glucose monitoring systems. The methodology proposed considers complete daily glucose profiles obtained from six patients with type 1 diabetes (T1D) who had their glucose monitored for eight weeks. The glucose profiles were distributed into the time spent in six different ranges. The time in one day is finite and limited to 24h, and the times spent in each of these different ranges are co-dependent and carry only relative information; therefore, CoDA is applied to these profiles. A K-means algorithm was applied to the coordinates obtained from the CoDA to obtain different patterns of days for each patient. Groups of days with relatively high time in the hypo and/or hyperglycaemic ranges and with different glucose variability were observed. Using CoDA of time in different ranges, individual glucose profiles were categorised into groups of days, which can be used by physicians to detect the different conditions of patients and personalise patient's insulin therapy according to each group. This approach can be useful to assist physicians and patients in managing the day-to-day variability that hinders glycaemic control. es_ES
dc.description.sponsorship The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This project has been partially supported by the Spanish Government MINECO through Grants DPI-2016-78831-C2-1-R, DPI2016-78831-C2-2-R, the National Council of Technological and Scientific Development, CNPq Brazil through Grants 202050/2015-7, 207688/2014-1 and EU through FEDER funds. es_ES
dc.language Inglés es_ES
dc.publisher SAGE Publications es_ES
dc.relation.ispartof Statistical Methods in Medical Research es_ES
dc.rights Reserva de todos los derechos es_ES
dc.subject Continuous glucose monitoring es_ES
dc.subject Type 1 diabetes es_ES
dc.subject Compositional data analysis es_ES
dc.subject Decision support system es_ES
dc.subject Diabetes management es_ES
dc.subject.classification INGENIERIA DE SISTEMAS Y AUTOMATICA es_ES
dc.title Individual Categorisation of Glucose Profiles Using Compositional Data Analysis es_ES
dc.type Artículo es_ES
dc.identifier.doi 10.1177/0962280218808819 es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CNPq//202050%2F2015-7/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//DPI2016-78831-C2-1-R/ES/SOLUCIONES PARA LA MEJORA DE LA EFICIENCIA Y SEGURIDAD DEL PANCREAS ARTIFICIAL MEDIANTE ARQUITECTURAS DE CONTROL MULTIVARIABLE TOLERANTES A FALLOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/MINECO//DPI2016-78831-C2-2-R/ES/SOLUCIONES PARA LA MEJORA DE LA EFICIENCIA Y SEGURIDAD DEL PANCREAS ARTIFICIAL MEDIANTE ARQUITECTURAS DE CONTROL MULTIVARIABLE TOLERANTES A FALLOS/ es_ES
dc.relation.projectID info:eu-repo/grantAgreement/CNPq//207688%2F2014-1/ es_ES
dc.rights.accessRights Cerrado es_ES
dc.contributor.affiliation Universitat Politècnica de València. Departamento de Ingeniería de Sistemas y Automática - Departament d'Enginyeria de Sistemes i Automàtica es_ES
dc.description.bibliographicCitation Biagi, L.; Bertachi, A.; Giménez, M.; Conget, I.; Bondía Company, J.; Martín-Fernández, JA.; Vehí, J. (2019). Individual Categorisation of Glucose Profiles Using Compositional Data Analysis. Statistical Methods in Medical Research. 28(12):3550-3567. https://doi.org/10.1177/0962280218808819 es_ES
dc.description.accrualMethod S es_ES
dc.relation.publisherversion https://doi.org/10.1177/0962280218808819 es_ES
dc.description.upvformatpinicio 3550 es_ES
dc.description.upvformatpfin 3567 es_ES
dc.type.version info:eu-repo/semantics/publishedVersion es_ES
dc.description.volume 28 es_ES
dc.description.issue 12 es_ES
dc.identifier.pmid 30380996 es_ES
dc.relation.pasarela S\386348 es_ES
dc.contributor.funder European Regional Development Fund es_ES
dc.contributor.funder Ministerio de Economía y Competitividad es_ES
dc.contributor.funder Conselho Nacional de Desenvolvimento Científico e Tecnológico, Brasil es_ES
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