DUGiDocsIndividualized Prediction of Blood Glucose Outcomes Using Compositional Data Analysis
dc.contributor.author
dc.date.accessioned
2023-11-15T11:23:10Z
dc.date.available
2023-11-15T11:23:10Z
dc.date.issued
2023-11-02
dc.identifier.uri
dc.description.abstract
This paper presents an individualized multiple linear regression model based on compositional data where we predict the mean and coefficient of variation of blood glucose in individuals with type 1 diabetes for the long-term (2 and 4 h). From these predictions, we estimate the minimum and maximum glucose values to provide future glycemic status. The proposed methodology has been validated using a dataset of 226 real adult patients with type 1 diabetes (Replace BG (NCT02258373)). The obtained results show a median balanced accuracy and sensitivity of over 90% and 80%, respectively. A information system has been implemented and validated to update patients on their glycemic status and associated risks for the next few hours
dc.description.sponsorship
This research was partially supported by grants PID2019-107722RB-C22 and PID2019-107722RB-C12 funded by MCIN/AEI/10.13039/501100011033, the Autonomous Government of Catalonia under Grant 2017 SGR 1551, and by the program for researchers in training at the University of Girona (IFUdG2019), in part by Generation and Transfer of Compositional Data Analysis Knowledge (CODA-GENERA). Ministerio de Ciencia e Innovación, Agencia Estatal de Investigación (MCIN/AEI/10.13039/501100011033) y FEDER Una manera de hacer Europa (Ref: PID2021-123833OB-I00; 1 September 2022–31 August 2025). Research Group “Compositional and Spatial Analysis” (COSDA). Agència de Gestió d’Ajuts Universitaris i de Recerca (AGAUR), Generalitat de Catalunya (Ref: 2021SGR01197). (2022–2024)
dc.format.mimetype
application/pdf
dc.language.iso
eng
dc.publisher
MDPI (Multidisciplinary Digital Publishing Institute)
dc.relation
PID2019-107722RB-C22
PID2021-123833OB-I00
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Reproducció digital del document publicat a: https://doi.org/10.3390/math11214517
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Mathematics, 2023, vol. 11, núm. 21, p. 4517
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Articles publicats (D-EEEiA)
dc.rights
Attribution 4.0 International
dc.rights.uri
dc.subject
dc.title
Individualized Prediction of Blood Glucose Outcomes Using Compositional Data Analysis
dc.type
info:eu-repo/semantics/article
dc.rights.accessRights
info:eu-repo/semantics/openAccess
dc.relation.projectID
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107722RB-C22/ES/PATIENT-TAILORED SOLUTIONS FOR BLOOD GLUCOSE CONTROL IN TYPE 1 DIABETES/
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2021-2023/PID2021-123833OB-I00/ES/GENERATION AND TRANSFER OF COMPOSITIONAL DATA ANALYSIS KNOWLEDGE/
dc.type.version
info:eu-repo/semantics/publishedVersion
dc.identifier.doi
dc.identifier.idgrec
037596
dc.contributor.funder
dc.type.peerreviewed
peer-reviewed
dc.relation.FundingProgramme
dc.relation.ProjectAcronym
dc.identifier.eissn
2227-7390