A simple method for recommending specialized specifications for diabetes monitoring

Autores
Avila, Luis Omar; Errecalde, Marcelo Luis
Año de publicación
2018
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
Under glycemic variability, a characterization of the desired blood glucose (BG) behavior is needed to assess if a given artificial pancreas (AP) respects its specification. The specification is an essential element to detect any deviation from an adequate insulin policy. Specializing the monitoring specification is therefore of utmost importance as existing guidelines for diabetes management are general and do not take into account how the personal factors and lifestyle affect the glycemic behavior. Surely, recommending personalized monitoring specifications may provide flexible and appropriate treatment goals to be attained by diabetic patients in order to account for their actual treatment needs. In this work, we use machine learning models to characterize glycemic behavior in synthetic healthy individuals. To account for the day-by-day fluctuation in BG levels, we use a stochastic process superimposed on a deterministic model of the glucose-insulin dynamics. The obtained characterization of the glycemic behavior in healthy individuals is then used as the target class to predict, and thus recommend, personalized monitoring specifications to diabetic patients. Results show that the approach stands as a feasible strategy to recommending appropriate and realistic monitoring goals for diabetic patients based on healthy individuals who share a similar glycemic behavior. Eventually, the incorporation of a recommender approach on an intelligent monitoring system for the AP will allow on-line adaptation of the treatment requirements for each patient.
Fil: Avila, Luis Omar. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina
Fil: Errecalde, Marcelo Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
Materia
COLD-START RECOMMENDATION
GLYCEMIC CONTROL
MACHINE LEARNING
MONITORING SPECIFICATION
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/148694
Acceder
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network_name_str CONICET Digital (CONICET)
spelling A simple method for recommending specialized specifications for diabetes monitoringAvila, Luis OmarErrecalde, Marcelo LuisCOLD-START RECOMMENDATIONGLYCEMIC CONTROLMACHINE LEARNINGMONITORING SPECIFICATIONhttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2Under glycemic variability, a characterization of the desired blood glucose (BG) behavior is needed to assess if a given artificial pancreas (AP) respects its specification. The specification is an essential element to detect any deviation from an adequate insulin policy. Specializing the monitoring specification is therefore of utmost importance as existing guidelines for diabetes management are general and do not take into account how the personal factors and lifestyle affect the glycemic behavior. Surely, recommending personalized monitoring specifications may provide flexible and appropriate treatment goals to be attained by diabetic patients in order to account for their actual treatment needs. In this work, we use machine learning models to characterize glycemic behavior in synthetic healthy individuals. To account for the day-by-day fluctuation in BG levels, we use a stochastic process superimposed on a deterministic model of the glucose-insulin dynamics. The obtained characterization of the glycemic behavior in healthy individuals is then used as the target class to predict, and thus recommend, personalized monitoring specifications to diabetic patients. Results show that the approach stands as a feasible strategy to recommending appropriate and realistic monitoring goals for diabetic patients based on healthy individuals who share a similar glycemic behavior. Eventually, the incorporation of a recommender approach on an intelligent monitoring system for the AP will allow on-line adaptation of the treatment requirements for each patient.Fil: Avila, Luis Omar. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; ArgentinaFil: Errecalde, Marcelo Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; ArgentinaPergamon-Elsevier Science Ltd2018-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/148694Avila, Luis Omar; Errecalde, Marcelo Luis; A simple method for recommending specialized specifications for diabetes monitoring; Pergamon-Elsevier Science Ltd; Expert Systems with Applications; 91; 1-2018; 298-3090957-41741873-6793CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0957417417306267info:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2017.09.019info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-03T10:00:37Zoai:ri.conicet.gov.ar:11336/148694instacron: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:34982025-09-03 10:00:37.556CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A simple method for recommending specialized specifications for diabetes monitoring
title A simple method for recommending specialized specifications for diabetes monitoring
spellingShingle A simple method for recommending specialized specifications for diabetes monitoring
Avila, Luis Omar
COLD-START RECOMMENDATION
GLYCEMIC CONTROL
MACHINE LEARNING
MONITORING SPECIFICATION
title_short A simple method for recommending specialized specifications for diabetes monitoring
title_full A simple method for recommending specialized specifications for diabetes monitoring
title_fullStr A simple method for recommending specialized specifications for diabetes monitoring
title_full_unstemmed A simple method for recommending specialized specifications for diabetes monitoring
title_sort A simple method for recommending specialized specifications for diabetes monitoring
dc.creator.none.fl_str_mv Avila, Luis Omar
Errecalde, Marcelo Luis
author Avila, Luis Omar
author_facet Avila, Luis Omar
Errecalde, Marcelo Luis
author_role author
author2 Errecalde, Marcelo Luis
author2_role author
dc.subject.none.fl_str_mv COLD-START RECOMMENDATION
GLYCEMIC CONTROL
MACHINE LEARNING
MONITORING SPECIFICATION
topic COLD-START RECOMMENDATION
GLYCEMIC CONTROL
MACHINE LEARNING
MONITORING SPECIFICATION
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv Under glycemic variability, a characterization of the desired blood glucose (BG) behavior is needed to assess if a given artificial pancreas (AP) respects its specification. The specification is an essential element to detect any deviation from an adequate insulin policy. Specializing the monitoring specification is therefore of utmost importance as existing guidelines for diabetes management are general and do not take into account how the personal factors and lifestyle affect the glycemic behavior. Surely, recommending personalized monitoring specifications may provide flexible and appropriate treatment goals to be attained by diabetic patients in order to account for their actual treatment needs. In this work, we use machine learning models to characterize glycemic behavior in synthetic healthy individuals. To account for the day-by-day fluctuation in BG levels, we use a stochastic process superimposed on a deterministic model of the glucose-insulin dynamics. The obtained characterization of the glycemic behavior in healthy individuals is then used as the target class to predict, and thus recommend, personalized monitoring specifications to diabetic patients. Results show that the approach stands as a feasible strategy to recommending appropriate and realistic monitoring goals for diabetic patients based on healthy individuals who share a similar glycemic behavior. Eventually, the incorporation of a recommender approach on an intelligent monitoring system for the AP will allow on-line adaptation of the treatment requirements for each patient.
Fil: Avila, Luis Omar. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - San Luis; Argentina
Fil: Errecalde, Marcelo Luis. Universidad Nacional de San Luis. Facultad de Ciencias Físico Matemáticas y Naturales. Departamento de Informática. Laboratorio Investigación y Desarrollo en Inteligencia Computacional; Argentina
description Under glycemic variability, a characterization of the desired blood glucose (BG) behavior is needed to assess if a given artificial pancreas (AP) respects its specification. The specification is an essential element to detect any deviation from an adequate insulin policy. Specializing the monitoring specification is therefore of utmost importance as existing guidelines for diabetes management are general and do not take into account how the personal factors and lifestyle affect the glycemic behavior. Surely, recommending personalized monitoring specifications may provide flexible and appropriate treatment goals to be attained by diabetic patients in order to account for their actual treatment needs. In this work, we use machine learning models to characterize glycemic behavior in synthetic healthy individuals. To account for the day-by-day fluctuation in BG levels, we use a stochastic process superimposed on a deterministic model of the glucose-insulin dynamics. The obtained characterization of the glycemic behavior in healthy individuals is then used as the target class to predict, and thus recommend, personalized monitoring specifications to diabetic patients. Results show that the approach stands as a feasible strategy to recommending appropriate and realistic monitoring goals for diabetic patients based on healthy individuals who share a similar glycemic behavior. Eventually, the incorporation of a recommender approach on an intelligent monitoring system for the AP will allow on-line adaptation of the treatment requirements for each patient.
publishDate 2018
dc.date.none.fl_str_mv 2018-01
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/11336/148694
Avila, Luis Omar; Errecalde, Marcelo Luis; A simple method for recommending specialized specifications for diabetes monitoring; Pergamon-Elsevier Science Ltd; Expert Systems with Applications; 91; 1-2018; 298-309
0957-4174
1873-6793
CONICET Digital
CONICET
url http://hdl.handle.net/11336/148694
identifier_str_mv Avila, Luis Omar; Errecalde, Marcelo Luis; A simple method for recommending specialized specifications for diabetes monitoring; Pergamon-Elsevier Science Ltd; Expert Systems with Applications; 91; 1-2018; 298-309
0957-4174
1873-6793
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S0957417417306267
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2017.09.019
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
publisher.none.fl_str_mv Pergamon-Elsevier Science Ltd
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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score 13.13397

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