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
- Institución
- Consejo Nacional de Investigaciones Científicas y Técnicas
- OAI Identificador
- oai:ri.conicet.gov.ar:11336/211622
Ver los metadatos del registro completo
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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 Técnicas2025-09-29T09:44:42Zoai: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:34982025-09-29 09:44:42.689CONICET 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 |
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info:eu-repo/semantics/openAccess https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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openAccess |
rights_invalid_str_mv |
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/ |
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application/pdf application/pdf |
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Institute of Electrical and Electronics Engineers |
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Institute of Electrical and Electronics Engineers |
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Consejo Nacional de Investigaciones Científicas y Técnicas |
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CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas |
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dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar |
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13.070432 |