Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis

Glycemia assessment in people with type 1 diabetes (T1D) has focused on the time spent in different glucose ranges. As this time reflects the relative contributions to the finite duration of a day, it should be treated as compositional data (CoDa) that can be applied to T1D data. Previous works pres...

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Authors: Cabrera, Alvis, Biagi, Lyvia, Beneyto Tantiña, Aleix, Estremera, Ernesto, Contreras, Ivan, Giménez, Marga, Conget, Ignacio, Bondia, Jorge, Martín Fernández, Josep Antoni, Vehí, Josep
Format: article
Status:Published version
Publication Date:2023
Country:España
Institution:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repository:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/22853
Online Access:http://hdl.handle.net/10256/22853
Access Level:Open access
Keyword:Diabetis
Diabetes
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
Monitoratge de pacients
Patient monitoring
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
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oai_identifier_str oai:recercat.cat:10256/22853
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spelling Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data AnalysisCabrera, AlvisBiagi, LyviaBeneyto Tantiña, AleixEstremera, ErnestoContreras, IvanGiménez, MargaConget, IgnacioBondia, JorgeMartín Fernández, Josep AntoniVehí, JosepDiabetisDiabetesGlucèmia -- Control automàticBlood sugar -- Automatic controlMonitoratge de pacientsPatient monitoringIntel·ligència artificial -- Aplicacions a la medicinaArtificial intelligence -- Medical applicationsGlycemia assessment in people with type 1 diabetes (T1D) has focused on the time spent in different glucose ranges. As this time reflects the relative contributions to the finite duration of a day, it should be treated as compositional data (CoDa) that can be applied to T1D data. Previous works presented a tool for the individual categorization of days and proposed a probabilistic transition model between categories, although validation has hitherto not been presented. In this study, we consider data from eight real adult patients with T1D obtained from continuous glucose monitoring (CGM) sensors and introduce a methodology based on compositional methods to validate the previously presented probability transition model. We conducted 5-fold cross-validation, with both the training and validation data being CoDa vectors, which requires developing new performance metrics. We design new accuracy and precision measures based on statistical error calculations. The results show that the precision for the entire model is higher than 95% in all patients. The use of a probabilistic transition model can help doctors and patients in diabetes treatment management and decision-making. Although the proposed method was tested with CoDa applied to T1D data obtained from CGM, the newly developed accuracy and precision measures apply to any other data or validation based on CoDaThis research was partially supported by grants PID2019-107722RB-C22 and PID2019-107722RB-C21 funded by MCIN/AEI/10.13039/501100011033, in part by the Autonomous Government of Catalonia under Grant 2017 SGR 1551, in part by the Spanish Ministry of Universities,and by the European Union through Next GenerationEU (Margarita Salas), and by the program for researchers in training at the University of Girona (IFUdG2019)MDPI (Multidisciplinary Digital Publishing Institute)Agencia Estatal de Investigación2023info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/22853http://hdl.handle.net/10256/22853Mathematics, 2023, vol. 11, núm. 5, p. 1241Articles publicats (D-EEEiA)reponame:Recercat. Dipósit de la Recerca de Catalunyainstname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)Inglésinfo:eu-repo/semantics/altIdentifier/doi/10.3390/math11051241info:eu-repo/semantics/altIdentifier/eissn/2227-7390PID2019-107722RB-C22info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107722RB-C22Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/228532026-05-29T05:05:01Z
dc.title.none.fl_str_mv Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis
title Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis
spellingShingle Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis
Cabrera, Alvis
Diabetis
Diabetes
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
Monitoratge de pacients
Patient monitoring
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
title_short Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis
title_full Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis
title_fullStr Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis
title_full_unstemmed Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis
title_sort Validation of a Probabilistic Prediction Model for Patients with Type 1 Diabetes Using Compositional Data Analysis
dc.creator.none.fl_str_mv Cabrera, Alvis
Biagi, Lyvia
Beneyto Tantiña, Aleix
Estremera, Ernesto
Contreras, Ivan
Giménez, Marga
Conget, Ignacio
Bondia, Jorge
Martín Fernández, Josep Antoni
Vehí, Josep
author Cabrera, Alvis
author_facet Cabrera, Alvis
Biagi, Lyvia
Beneyto Tantiña, Aleix
Estremera, Ernesto
Contreras, Ivan
Giménez, Marga
Conget, Ignacio
Bondia, Jorge
Martín Fernández, Josep Antoni
Vehí, Josep
author_role author
author2 Biagi, Lyvia
Beneyto Tantiña, Aleix
Estremera, Ernesto
Contreras, Ivan
Giménez, Marga
Conget, Ignacio
Bondia, Jorge
Martín Fernández, Josep Antoni
Vehí, Josep
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación
dc.subject.none.fl_str_mv Diabetis
Diabetes
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
Monitoratge de pacients
Patient monitoring
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
topic Diabetis
Diabetes
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
Monitoratge de pacients
Patient monitoring
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
description Glycemia assessment in people with type 1 diabetes (T1D) has focused on the time spent in different glucose ranges. As this time reflects the relative contributions to the finite duration of a day, it should be treated as compositional data (CoDa) that can be applied to T1D data. Previous works presented a tool for the individual categorization of days and proposed a probabilistic transition model between categories, although validation has hitherto not been presented. In this study, we consider data from eight real adult patients with T1D obtained from continuous glucose monitoring (CGM) sensors and introduce a methodology based on compositional methods to validate the previously presented probability transition model. We conducted 5-fold cross-validation, with both the training and validation data being CoDa vectors, which requires developing new performance metrics. We design new accuracy and precision measures based on statistical error calculations. The results show that the precision for the entire model is higher than 95% in all patients. The use of a probabilistic transition model can help doctors and patients in diabetes treatment management and decision-making. Although the proposed method was tested with CoDa applied to T1D data obtained from CGM, the newly developed accuracy and precision measures apply to any other data or validation based on CoDa
publishDate 2023
dc.date.none.fl_str_mv 2023
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
peer-reviewed
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10256/22853
http://hdl.handle.net/10256/22853
url http://hdl.handle.net/10256/22853
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.3390/math11051241
info:eu-repo/semantics/altIdentifier/eissn/2227-7390
PID2019-107722RB-C22
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107722RB-C22
dc.rights.none.fl_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
info:eu-repo/semantics/openAccess
rights_invalid_str_mv Attribution 4.0 International
http://creativecommons.org/licenses/by/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI (Multidisciplinary Digital Publishing Institute)
publisher.none.fl_str_mv MDPI (Multidisciplinary Digital Publishing Institute)
dc.source.none.fl_str_mv Mathematics, 2023, vol. 11, núm. 5, p. 1241
Articles publicats (D-EEEiA)
reponame:Recercat. Dipósit de la Recerca de Catalunya
instname:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
instname_str Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
reponame_str Recercat. Dipósit de la Recerca de Catalunya
collection Recercat. Dipósit de la Recerca de Catalunya
repository.name.fl_str_mv
repository.mail.fl_str_mv
_version_ 1869411788107284480
score 15.812429

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