Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models

In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of augmenting continuous glucose monitor data from individual patients. We use these synthetic data with the aim of improving the overall performance of pre...

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Detalles Bibliográficos
Autores: Noguer, Josep, Contreras, Ivan, Mujahid, Omer, Beneyto Tantiña, Aleix, Vehí, Josep
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Varias* (Consorci de Biblioteques Universitáries de Catalunya, Centre de Serveis Científics i Acadèmics de Catalunya)
Repositorio:Recercat. Dipósit de la Recerca de Catalunya
OAI Identifier:oai:recercat.cat:10256/21277
Acceso en línea:http://hdl.handle.net/10256/21277
Access Level:acceso abierto
Palabra clave:Monitoratge de pacients
Patient monitoring
Diabetis
Diabetes
Aprenentatge automàtic
Machine learning
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
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oai_identifier_str oai:recercat.cat:10256/21277
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network_name_str España
repository_id_str
spelling Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning ModelsNoguer, JosepContreras, IvanMujahid, OmerBeneyto Tantiña, AleixVehí, JosepMonitoratge de pacientsPatient monitoringDiabetisDiabetesAprenentatge automàticMachine learningIntel·ligència artificial -- Aplicacions a la medicinaArtificial intelligence -- Medical applicationsGlucèmia -- Control automàticBlood sugar -- Automatic controlIn this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of augmenting continuous glucose monitor data from individual patients. We use these synthetic data with the aim of improving the overall performance of prediction models based on machine learning techniques. Experiments were performed on two cohorts of patients suffering from type 1 diabetes mellitus with significant differences in their clinical outcomes. In the first contribution, we have demonstrated that the chosen methodology is able to replicate the intrinsic characteristics of individual patients following the statistical distributions of the original data. Next, a second contribution demonstrates the potential of synthetic data to improve the performance of machine learning approaches by testing and comparing different prediction models for the problem of predicting nocturnal hypoglycemic events in type 1 diabetic patients. The results obtained for both generative and predictive models are quite encouraging and set a precedent in the use of generative techniques to train new machine learning modelsThis work was partially supported by the Spanish Ministry of Science and Innovation through grant [PID2019-107722RB-C22 /AEI/10.13039/501100011033]; [PID2020-117171RA-I00 funded by MCIN/AEI/10.13039/501100011033]; the Government of Catalonia under [2017SGR1551]MDPI (Multidisciplinary Digital Publishing Institute)Agencia Estatal de Investigación2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/21277Sensors, 2022, vol. 22, núm. 13, p. 4944Articles publicats (IIIA)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/s22134944info:eu-repo/semantics/altIdentifier/eissn/1424-8220PID2019-107722RB-C22PID2020-117171RA-I00info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107722RB-C22info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117171RA-I00Attribution 4.0 Internationalhttp://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccessoai:recercat.cat:10256/212772026-05-29T05:05:01Z
dc.title.none.fl_str_mv Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models
title Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models
spellingShingle Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models
Noguer, Josep
Monitoratge de pacients
Patient monitoring
Diabetis
Diabetes
Aprenentatge automàtic
Machine learning
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
title_short Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models
title_full Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models
title_fullStr Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models
title_full_unstemmed Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models
title_sort Generation of Individualized Synthetic Data for Augmentation of the Type 1 Diabetes Data Sets Using Deep Learning Models
dc.creator.none.fl_str_mv Noguer, Josep
Contreras, Ivan
Mujahid, Omer
Beneyto Tantiña, Aleix
Vehí, Josep
author Noguer, Josep
author_facet Noguer, Josep
Contreras, Ivan
Mujahid, Omer
Beneyto Tantiña, Aleix
Vehí, Josep
author_role author
author2 Contreras, Ivan
Mujahid, Omer
Beneyto Tantiña, Aleix
Vehí, Josep
author2_role author
author
author
author
dc.contributor.none.fl_str_mv Agencia Estatal de Investigación
dc.subject.none.fl_str_mv Monitoratge de pacients
Patient monitoring
Diabetis
Diabetes
Aprenentatge automàtic
Machine learning
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
topic Monitoratge de pacients
Patient monitoring
Diabetis
Diabetes
Aprenentatge automàtic
Machine learning
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
description In this paper, we present a methodology based on generative adversarial network architecture to generate synthetic data sets with the intention of augmenting continuous glucose monitor data from individual patients. We use these synthetic data with the aim of improving the overall performance of prediction models based on machine learning techniques. Experiments were performed on two cohorts of patients suffering from type 1 diabetes mellitus with significant differences in their clinical outcomes. In the first contribution, we have demonstrated that the chosen methodology is able to replicate the intrinsic characteristics of individual patients following the statistical distributions of the original data. Next, a second contribution demonstrates the potential of synthetic data to improve the performance of machine learning approaches by testing and comparing different prediction models for the problem of predicting nocturnal hypoglycemic events in type 1 diabetic patients. The results obtained for both generative and predictive models are quite encouraging and set a precedent in the use of generative techniques to train new machine learning models
publishDate 2022
dc.date.none.fl_str_mv 2022
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/10256/21277
url http://hdl.handle.net/10256/21277
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/s22134944
info:eu-repo/semantics/altIdentifier/eissn/1424-8220
PID2019-107722RB-C22
PID2020-117171RA-I00
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-107722RB-C22
info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-117171RA-I00
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 Sensors, 2022, vol. 22, núm. 13, p. 4944
Articles publicats (IIIA)
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
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score 15.81155

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