Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models

Mathematical modeling of the glucose–insulin system forms the core of simulators in the field of glucose metabolism. The complexity of human biological systems makes it a challenging task for the physiological models to encompass the entirety of such systems. Even though modern diabetes simulators p...

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Detalles Bibliográficos
Autores: Mujahid, Omer, Contreras, Ivan, Beneyto Tantiña, Aleix, Conget, Ignacio, Giménez, Marga, 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:10256/21872
Acceso en línea:http://hdl.handle.net/10256/21872
Access Level:acceso abierto
Palabra clave:Monitoratge de pacients
Patient monitoring
Diabetis
Diabetes
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
Simulació (Medicina)
Malingering
Glucèmia -- Models matemàtics
Blood sugar -- Mathematical models
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
id ES_f97df76bbdad3dc35db17fdd643eb0dc
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spelling Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative ModelsMujahid, OmerContreras, IvanBeneyto Tantiña, AleixConget, IgnacioGiménez, MargaVehí, JosepMonitoratge de pacientsPatient monitoringDiabetisDiabetesIntel·ligència artificial -- Aplicacions a la medicinaArtificial intelligence -- Medical applicationsSimulació (Medicina)MalingeringGlucèmia -- Models matemàticsBlood sugar -- Mathematical modelsGlucèmia -- Control automàticBlood sugar -- Automatic controlMathematical modeling of the glucose–insulin system forms the core of simulators in the field of glucose metabolism. The complexity of human biological systems makes it a challenging task for the physiological models to encompass the entirety of such systems. Even though modern diabetes simulators perform a respectable task of simulating the glucose–insulin action, they are unable to estimate various phenomena affecting the glycemic profile of an individual such as glycemic disturbances and patient behavior. This research work presents a potential solution to this problem by proposing a method for the generation of blood glucose values conditioned on plasma insulin approximation of type 1 diabetes patients using a pixel-to-pixel generative adversarial network. Two type-1 diabetes cohorts comprising 29 and 6 patients, respectively, are used to train the generative model. This study shows that the generated blood glucose values are statistically similar to the real blood glucose values, mimicking the time-in-range results for each of the standard blood glucose ranges in type 1 diabetes management and obtaining similar means and variability outcomes. Furthermore, the causal relationship between the plasma insulin values and the generated blood glucose conforms to the same relationship observed in real patients. These results herald the aptness of deep generative models for the generation of virtual patients with diabetesThis work was partially supported by the Spanish Ministry of Universities, the European Union through Next GenerationEU (Margarita Salas), the Spanish Ministry of Science and Innovation through grant PID2019107722RBC22/AEI/10.13039/501100011033, PID2020-117171RA-I00 funded by MCIN/AEI/10.13039/501100011033 and the Government of Catalonia under 2017SGR1551 and 2020 FI_B 00965MDPI (Multidisciplinary Digital Publishing Institute)Agencia Estatal de Investigación2022info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionpeer-reviewedapplication/pdfhttp://hdl.handle.net/10256/21872http://hdl.handle.net/10256/21872Mathematics, 2022, vol. 10, núm. 20, p. 3741Articles 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/math10203741info:eu-repo/semantics/altIdentifier/eissn/2227-7390PID2019-107722RB-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:10256/218722026-05-29T05:05:01Z
dc.title.none.fl_str_mv Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models
title Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models
spellingShingle Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models
Mujahid, Omer
Monitoratge de pacients
Patient monitoring
Diabetis
Diabetes
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
Simulació (Medicina)
Malingering
Glucèmia -- Models matemàtics
Blood sugar -- Mathematical models
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
title_short Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models
title_full Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models
title_fullStr Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models
title_full_unstemmed Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models
title_sort Conditional Synthesis of Blood Glucose Profiles for T1D Patients Using Deep Generative Models
dc.creator.none.fl_str_mv Mujahid, Omer
Contreras, Ivan
Beneyto Tantiña, Aleix
Conget, Ignacio
Giménez, Marga
Vehí, Josep
author Mujahid, Omer
author_facet Mujahid, Omer
Contreras, Ivan
Beneyto Tantiña, Aleix
Conget, Ignacio
Giménez, Marga
Vehí, Josep
author_role author
author2 Contreras, Ivan
Beneyto Tantiña, Aleix
Conget, Ignacio
Giménez, Marga
Vehí, Josep
author2_role author
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
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
Simulació (Medicina)
Malingering
Glucèmia -- Models matemàtics
Blood sugar -- Mathematical models
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
topic Monitoratge de pacients
Patient monitoring
Diabetis
Diabetes
Intel·ligència artificial -- Aplicacions a la medicina
Artificial intelligence -- Medical applications
Simulació (Medicina)
Malingering
Glucèmia -- Models matemàtics
Blood sugar -- Mathematical models
Glucèmia -- Control automàtic
Blood sugar -- Automatic control
description Mathematical modeling of the glucose–insulin system forms the core of simulators in the field of glucose metabolism. The complexity of human biological systems makes it a challenging task for the physiological models to encompass the entirety of such systems. Even though modern diabetes simulators perform a respectable task of simulating the glucose–insulin action, they are unable to estimate various phenomena affecting the glycemic profile of an individual such as glycemic disturbances and patient behavior. This research work presents a potential solution to this problem by proposing a method for the generation of blood glucose values conditioned on plasma insulin approximation of type 1 diabetes patients using a pixel-to-pixel generative adversarial network. Two type-1 diabetes cohorts comprising 29 and 6 patients, respectively, are used to train the generative model. This study shows that the generated blood glucose values are statistically similar to the real blood glucose values, mimicking the time-in-range results for each of the standard blood glucose ranges in type 1 diabetes management and obtaining similar means and variability outcomes. Furthermore, the causal relationship between the plasma insulin values and the generated blood glucose conforms to the same relationship observed in real patients. These results herald the aptness of deep generative models for the generation of virtual patients with diabetes
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/10256/21872
http://hdl.handle.net/10256/21872
url http://hdl.handle.net/10256/21872
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/math10203741
info:eu-repo/semantics/altIdentifier/eissn/2227-7390
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 Mathematics, 2022, vol. 10, núm. 20, p. 3741
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
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score 15.81155

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