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dc.contributor.author
Hoyos, J. D.
dc.contributor.author
Villa Tamayo, M. F.
dc.contributor.author
Builes Montano, C. E.
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Ramirez Rincon, A.
dc.contributor.author
Godoy, José Luis
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dc.contributor.author
Garcia Tirado, J.
dc.contributor.author
Rivadeneira Paz, Pablo Santiago
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dc.date.available
2023-09-15T12:39:55Z
dc.date.issued
2021-04
dc.identifier.citation
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
dc.identifier.issn
2169-3536
dc.identifier.uri
http://hdl.handle.net/11336/211622
dc.description.abstract
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.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Institute of Electrical and Electronics Engineers
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
BIOMEDICAL SYSTEMS
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GLUCOSE DYNAMICS
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IDENTIFIABILITY
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MODEL IDENTIFICATION
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PRACTICAL INDENTIFIABILITY
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Otras Ingeniería Médica
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dc.subject.classification
Ingeniería Médica
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dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
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dc.title
Identifiability of Control-Oriented Glucose-Insulin Linear Models: Review and Analysis
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2023-09-13T12:08:07Z
dc.journal.volume
9
dc.journal.pagination
69173-69188
dc.journal.pais
Países Bajos
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dc.description.fil
Fil: Hoyos, J. D.. Universidad Nacional de Colombia. Sede Medellín; Colombia
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Fil: Villa Tamayo, M. F.. Universidad Nacional de Colombia. Sede Medellín; Colombia
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Fil: Builes Montano, C. E.. Universidad de Antioquia; Colombia
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Fil: Ramirez Rincon, A.. Universidad Pontificia Bolivariana; Colombia
dc.description.fil
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
dc.description.fil
Fil: Garcia Tirado, J.. University of Virginia; Estados Unidos
dc.description.fil
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
dc.journal.title
IEEE Access
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1109/ACCESS.2021.3076405
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