Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis

Autores
Hoyos, J. D.; Villa Tamayo, M. F.; Builes Montano, C. E.; Ramirez Rincon, A.; Godoy, José Luis; Garcia Tirado, J.; Rivadeneira Paz, Pablo Santiago
Año de publicación
2021
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
One of the main challenges of glucose control in patients with type 1 diabetes is identifying a control-oriented model that reliably predicts the behavior of glycemia. Here, a review is provided emphasizing the structural identifiability and observability properties, which surprisingly reveals that few of them are globally identifiable and observable at the same time. Thus, a general proposal was developed to encompass four linear models according to suitable assumptions and transformations. After the corresponding structural properties analysis, two minimal model structures are generated, which are globally identifiable and observable. Then, the practical identifiability is analyzed for this application showing that the standard collected data in many cases do not have the necessary quality to ensure a unique solution in the identification process even when a considerable amount of data is collected. The two minimal control-oriented models were identified using a standard identification procedure using data from 30 virtual patients of the UVA/Padova simulator and 77 diabetes care data from adult patients of a diabetes center. The identification was performed in two stages: calibration and validation. In the first stage, the average length was taken as two days (dictated by the practical identifiability). For both structures, the mean absolute error was 16.8 mg/dl and 9.9 mg/dl for virtual patients and 21.6 mg/dl and 21.5 mg/dl for real patients. For the second stage, a one-day validation window was considered long enough for future artificial pancreas applications. The mean absolute error was 23.9 mg/dl and 12.3 mg/dl for virtual patients and 39.2 mg/dl and 36.6 mg/dl for virtual and real patients. These results confirm that linear models can be used as prediction models in model-based control strategies as predictive control.
Fil: Hoyos, J. D.. Universidad Nacional de Colombia. Sede Medellín; Colombia
Fil: Villa Tamayo, M. F.. Universidad Nacional de Colombia. Sede Medellín; Colombia
Fil: Builes Montano, C. E.. Universidad de Antioquia; Colombia
Fil: Ramirez Rincon, A.. Universidad Pontificia Bolivariana; Colombia
Fil: Godoy, José Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Garcia Tirado, J.. University of Virginia; Estados Unidos
Fil: Rivadeneira Paz, Pablo Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Materia
BIOMEDICAL SYSTEMS
GLUCOSE DYNAMICS
IDENTIFIABILITY
MODEL IDENTIFICATION
PRACTICAL INDENTIFIABILITY
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/211622
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/211622
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repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and AnalysisHoyos, J. D.Villa Tamayo, M. F.Builes Montano, C. E.Ramirez Rincon, A.Godoy, José LuisGarcia Tirado, J.Rivadeneira Paz, Pablo SantiagoBIOMEDICAL SYSTEMSGLUCOSE DYNAMICSIDENTIFIABILITYMODEL IDENTIFICATIONPRACTICAL INDENTIFIABILITYhttps://purl.org/becyt/ford/2.6https://purl.org/becyt/ford/2One of the main challenges of glucose control in patients with type 1 diabetes is identifying a control-oriented model that reliably predicts the behavior of glycemia. Here, a review is provided emphasizing the structural identifiability and observability properties, which surprisingly reveals that few of them are globally identifiable and observable at the same time. Thus, a general proposal was developed to encompass four linear models according to suitable assumptions and transformations. After the corresponding structural properties analysis, two minimal model structures are generated, which are globally identifiable and observable. Then, the practical identifiability is analyzed for this application showing that the standard collected data in many cases do not have the necessary quality to ensure a unique solution in the identification process even when a considerable amount of data is collected. The two minimal control-oriented models were identified using a standard identification procedure using data from 30 virtual patients of the UVA/Padova simulator and 77 diabetes care data from adult patients of a diabetes center. The identification was performed in two stages: calibration and validation. In the first stage, the average length was taken as two days (dictated by the practical identifiability). For both structures, the mean absolute error was 16.8 mg/dl and 9.9 mg/dl for virtual patients and 21.6 mg/dl and 21.5 mg/dl for real patients. For the second stage, a one-day validation window was considered long enough for future artificial pancreas applications. The mean absolute error was 23.9 mg/dl and 12.3 mg/dl for virtual patients and 39.2 mg/dl and 36.6 mg/dl for virtual and real patients. These results confirm that linear models can be used as prediction models in model-based control strategies as predictive control.Fil: Hoyos, J. D.. Universidad Nacional de Colombia. Sede Medellín; ColombiaFil: Villa Tamayo, M. F.. Universidad Nacional de Colombia. Sede Medellín; ColombiaFil: Builes Montano, C. E.. Universidad de Antioquia; ColombiaFil: Ramirez Rincon, A.. Universidad Pontificia Bolivariana; ColombiaFil: Godoy, José Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaFil: Garcia Tirado, J.. University of Virginia; Estados UnidosFil: Rivadeneira Paz, Pablo Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; ArgentinaInstitute of Electrical and Electronics Engineers2021-04info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/211622Hoyos, J. D.; Villa Tamayo, M. F.; Builes Montano, C. E.; Ramirez Rincon, A.; Godoy, José Luis; et al.; Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis; Institute of Electrical and Electronics Engineers; IEEE Access; 9; 4-2021; 69173-691882169-3536CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1109/ACCESS.2021.3076405info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-sa/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-22T11:13:32Zoai:ri.conicet.gov.ar:11336/211622instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-10-22 11:13:32.682CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis
title Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis
spellingShingle Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis
Hoyos, J. D.
BIOMEDICAL SYSTEMS
GLUCOSE DYNAMICS
IDENTIFIABILITY
MODEL IDENTIFICATION
PRACTICAL INDENTIFIABILITY
title_short Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis
title_full Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis
title_fullStr Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis
title_full_unstemmed Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis
title_sort Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis
dc.creator.none.fl_str_mv Hoyos, J. D.
Villa Tamayo, M. F.
Builes Montano, C. E.
Ramirez Rincon, A.
Godoy, José Luis
Garcia Tirado, J.
Rivadeneira Paz, Pablo Santiago
author Hoyos, J. D.
author_facet Hoyos, J. D.
Villa Tamayo, M. F.
Builes Montano, C. E.
Ramirez Rincon, A.
Godoy, José Luis
Garcia Tirado, J.
Rivadeneira Paz, Pablo Santiago
author_role author
author2 Villa Tamayo, M. F.
Builes Montano, C. E.
Ramirez Rincon, A.
Godoy, José Luis
Garcia Tirado, J.
Rivadeneira Paz, Pablo Santiago
author2_role author
author
author
author
author
author
dc.subject.none.fl_str_mv BIOMEDICAL SYSTEMS
GLUCOSE DYNAMICS
IDENTIFIABILITY
MODEL IDENTIFICATION
PRACTICAL INDENTIFIABILITY
topic BIOMEDICAL SYSTEMS
GLUCOSE DYNAMICS
IDENTIFIABILITY
MODEL IDENTIFICATION
PRACTICAL INDENTIFIABILITY
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.6
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv One of the main challenges of glucose control in patients with type 1 diabetes is identifying a control-oriented model that reliably predicts the behavior of glycemia. Here, a review is provided emphasizing the structural identifiability and observability properties, which surprisingly reveals that few of them are globally identifiable and observable at the same time. Thus, a general proposal was developed to encompass four linear models according to suitable assumptions and transformations. After the corresponding structural properties analysis, two minimal model structures are generated, which are globally identifiable and observable. Then, the practical identifiability is analyzed for this application showing that the standard collected data in many cases do not have the necessary quality to ensure a unique solution in the identification process even when a considerable amount of data is collected. The two minimal control-oriented models were identified using a standard identification procedure using data from 30 virtual patients of the UVA/Padova simulator and 77 diabetes care data from adult patients of a diabetes center. The identification was performed in two stages: calibration and validation. In the first stage, the average length was taken as two days (dictated by the practical identifiability). For both structures, the mean absolute error was 16.8 mg/dl and 9.9 mg/dl for virtual patients and 21.6 mg/dl and 21.5 mg/dl for real patients. For the second stage, a one-day validation window was considered long enough for future artificial pancreas applications. The mean absolute error was 23.9 mg/dl and 12.3 mg/dl for virtual patients and 39.2 mg/dl and 36.6 mg/dl for virtual and real patients. These results confirm that linear models can be used as prediction models in model-based control strategies as predictive control.
Fil: Hoyos, J. D.. Universidad Nacional de Colombia. Sede Medellín; Colombia
Fil: Villa Tamayo, M. F.. Universidad Nacional de Colombia. Sede Medellín; Colombia
Fil: Builes Montano, C. E.. Universidad de Antioquia; Colombia
Fil: Ramirez Rincon, A.. Universidad Pontificia Bolivariana; Colombia
Fil: Godoy, José Luis. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
Fil: Garcia Tirado, J.. University of Virginia; Estados Unidos
Fil: Rivadeneira Paz, Pablo Santiago. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
description One of the main challenges of glucose control in patients with type 1 diabetes is identifying a control-oriented model that reliably predicts the behavior of glycemia. Here, a review is provided emphasizing the structural identifiability and observability properties, which surprisingly reveals that few of them are globally identifiable and observable at the same time. Thus, a general proposal was developed to encompass four linear models according to suitable assumptions and transformations. After the corresponding structural properties analysis, two minimal model structures are generated, which are globally identifiable and observable. Then, the practical identifiability is analyzed for this application showing that the standard collected data in many cases do not have the necessary quality to ensure a unique solution in the identification process even when a considerable amount of data is collected. The two minimal control-oriented models were identified using a standard identification procedure using data from 30 virtual patients of the UVA/Padova simulator and 77 diabetes care data from adult patients of a diabetes center. The identification was performed in two stages: calibration and validation. In the first stage, the average length was taken as two days (dictated by the practical identifiability). For both structures, the mean absolute error was 16.8 mg/dl and 9.9 mg/dl for virtual patients and 21.6 mg/dl and 21.5 mg/dl for real patients. For the second stage, a one-day validation window was considered long enough for future artificial pancreas applications. The mean absolute error was 23.9 mg/dl and 12.3 mg/dl for virtual patients and 39.2 mg/dl and 36.6 mg/dl for virtual and real patients. These results confirm that linear models can be used as prediction models in model-based control strategies as predictive control.
publishDate 2021
dc.date.none.fl_str_mv 2021-04
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/211622
Hoyos, J. D.; Villa Tamayo, M. F.; Builes Montano, C. E.; Ramirez Rincon, A.; Godoy, José Luis; et al.; Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis; Institute of Electrical and Electronics Engineers; IEEE Access; 9; 4-2021; 69173-69188
2169-3536
CONICET Digital
CONICET
url http://hdl.handle.net/11336/211622
identifier_str_mv Hoyos, J. D.; Villa Tamayo, M. F.; Builes Montano, C. E.; Ramirez Rincon, A.; Godoy, José Luis; et al.; Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis; Institute of Electrical and Electronics Engineers; IEEE Access; 9; 4-2021; 69173-69188
2169-3536
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1109/ACCESS.2021.3076405
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
dc.publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
publisher.none.fl_str_mv Institute of Electrical and Electronics Engineers
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
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