On-line policy learning and adaptation for real-time personalization of an artificial pancreas

Autores
de Paula, Mariano; Acosta, Gerardo Gabriel; Martinez, Ernesto Carlos
Año de publicación
2014
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The dynamic complexity of the glucose-insulin metabolism in diabetic patients is the main obstacle towards widespread use of an artificial pancreas. The significant level of subject-specific glycemic variability requires continuously adapting the control policy to successfully face daily changes in patient´s metabolism and lifestyle. In this paper, an on-line selective reinforcement learning algorithm that enables real-time adaptation of a control policy based on ongoing interactions with the patient so as to tailor the artificial pancreas is proposed. Adaptation includes two online procedures: on-line sparsification and parameter updating of the Gaussian process used to approximate the control policy. With the proposed sparsification method, the support data dictionary for on-line learning is modified by checking if in the arriving data stream there exists novel information to be added to the dictionary in order to personalize the policy. Results obtained in silico experiments demonstrate that on-line policy learning is both safe and efficient for maintaining blood glucose variability within the normoglycemic range.
Fil: de Paula, Mariano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Universidad Nacional del Centro de la Pcia.de Bs.as.. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tandil. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernacion. Comision de Invest.cientificas. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires; Argentina
Fil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingenieria Olavarria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
Materia
Diabetes
Gaussian Processes
Glycemic Variability
On-Line Sparsification
Policy Learning
Reinforcement Learning
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:11336/22463
Acceder
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network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling On-line policy learning and adaptation for real-time personalization of an artificial pancreasde Paula, MarianoAcosta, Gerardo GabrielMartinez, Ernesto CarlosDiabetesGaussian ProcessesGlycemic VariabilityOn-Line SparsificationPolicy LearningReinforcement Learninghttps://purl.org/becyt/ford/2.2https://purl.org/becyt/ford/2The dynamic complexity of the glucose-insulin metabolism in diabetic patients is the main obstacle towards widespread use of an artificial pancreas. The significant level of subject-specific glycemic variability requires continuously adapting the control policy to successfully face daily changes in patient´s metabolism and lifestyle. In this paper, an on-line selective reinforcement learning algorithm that enables real-time adaptation of a control policy based on ongoing interactions with the patient so as to tailor the artificial pancreas is proposed. Adaptation includes two online procedures: on-line sparsification and parameter updating of the Gaussian process used to approximate the control policy. With the proposed sparsification method, the support data dictionary for on-line learning is modified by checking if in the arriving data stream there exists novel information to be added to the dictionary in order to personalize the policy. Results obtained in silico experiments demonstrate that on-line policy learning is both safe and efficient for maintaining blood glucose variability within the normoglycemic range.Fil: de Paula, Mariano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Universidad Nacional del Centro de la Pcia.de Bs.as.. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tandil. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernacion. Comision de Invest.cientificas. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires; ArgentinaFil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingenieria Olavarria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; ArgentinaFil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; ArgentinaElsevier2014-10info: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/11336/22463de Paula, Mariano; Acosta, Gerardo Gabriel; Martinez, Ernesto Carlos; On-line policy learning and adaptation for real-time personalization of an artificial pancreas; Elsevier; Expert Systems with Applications; 42; 4; 10-2014; 2234-22550957-4174CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2014.10.038info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0957417414006629info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by-nc-nd/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-10-15T15:14:59Zoai:ri.conicet.gov.ar:11336/22463instacron: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:34982025-10-15 15:15:00.058CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv On-line policy learning and adaptation for real-time personalization of an artificial pancreas
title On-line policy learning and adaptation for real-time personalization of an artificial pancreas
spellingShingle On-line policy learning and adaptation for real-time personalization of an artificial pancreas
de Paula, Mariano
Diabetes
Gaussian Processes
Glycemic Variability
On-Line Sparsification
Policy Learning
Reinforcement Learning
title_short On-line policy learning and adaptation for real-time personalization of an artificial pancreas
title_full On-line policy learning and adaptation for real-time personalization of an artificial pancreas
title_fullStr On-line policy learning and adaptation for real-time personalization of an artificial pancreas
title_full_unstemmed On-line policy learning and adaptation for real-time personalization of an artificial pancreas
title_sort On-line policy learning and adaptation for real-time personalization of an artificial pancreas
dc.creator.none.fl_str_mv de Paula, Mariano
Acosta, Gerardo Gabriel
Martinez, Ernesto Carlos
author de Paula, Mariano
author_facet de Paula, Mariano
Acosta, Gerardo Gabriel
Martinez, Ernesto Carlos
author_role author
author2 Acosta, Gerardo Gabriel
Martinez, Ernesto Carlos
author2_role author
author
dc.subject.none.fl_str_mv Diabetes
Gaussian Processes
Glycemic Variability
On-Line Sparsification
Policy Learning
Reinforcement Learning
topic Diabetes
Gaussian Processes
Glycemic Variability
On-Line Sparsification
Policy Learning
Reinforcement Learning
purl_subject.fl_str_mv https://purl.org/becyt/ford/2.2
https://purl.org/becyt/ford/2
dc.description.none.fl_txt_mv The dynamic complexity of the glucose-insulin metabolism in diabetic patients is the main obstacle towards widespread use of an artificial pancreas. The significant level of subject-specific glycemic variability requires continuously adapting the control policy to successfully face daily changes in patient´s metabolism and lifestyle. In this paper, an on-line selective reinforcement learning algorithm that enables real-time adaptation of a control policy based on ongoing interactions with the patient so as to tailor the artificial pancreas is proposed. Adaptation includes two online procedures: on-line sparsification and parameter updating of the Gaussian process used to approximate the control policy. With the proposed sparsification method, the support data dictionary for on-line learning is modified by checking if in the arriving data stream there exists novel information to be added to the dictionary in order to personalize the policy. Results obtained in silico experiments demonstrate that on-line policy learning is both safe and efficient for maintaining blood glucose variability within the normoglycemic range.
Fil: de Paula, Mariano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Universidad Nacional del Centro de la Pcia.de Bs.as.. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. - Consejo Nacional de Investigaciones Cientificas y Tecnicas. Centro Cientifico Tecnologico Conicet - Tandil. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires. - Provincia de Buenos Aires. Gobernacion. Comision de Invest.cientificas. Centro de Investigaciones En Fisica E Ingenieria del Centro de la Provincia de Buenos Aires; Argentina
Fil: Acosta, Gerardo Gabriel. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingenieria Olavarria; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
Fil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo y Diseño. Universidad Tecnológica Nacional. Facultad Regional Santa Fe. Instituto de Desarrollo y Diseño; Argentina
description The dynamic complexity of the glucose-insulin metabolism in diabetic patients is the main obstacle towards widespread use of an artificial pancreas. The significant level of subject-specific glycemic variability requires continuously adapting the control policy to successfully face daily changes in patient´s metabolism and lifestyle. In this paper, an on-line selective reinforcement learning algorithm that enables real-time adaptation of a control policy based on ongoing interactions with the patient so as to tailor the artificial pancreas is proposed. Adaptation includes two online procedures: on-line sparsification and parameter updating of the Gaussian process used to approximate the control policy. With the proposed sparsification method, the support data dictionary for on-line learning is modified by checking if in the arriving data stream there exists novel information to be added to the dictionary in order to personalize the policy. Results obtained in silico experiments demonstrate that on-line policy learning is both safe and efficient for maintaining blood glucose variability within the normoglycemic range.
publishDate 2014
dc.date.none.fl_str_mv 2014-10
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/11336/22463
de Paula, Mariano; Acosta, Gerardo Gabriel; Martinez, Ernesto Carlos; On-line policy learning and adaptation for real-time personalization of an artificial pancreas; Elsevier; Expert Systems with Applications; 42; 4; 10-2014; 2234-2255
0957-4174
CONICET Digital
CONICET
url http://hdl.handle.net/11336/22463
identifier_str_mv de Paula, Mariano; Acosta, Gerardo Gabriel; Martinez, Ernesto Carlos; On-line policy learning and adaptation for real-time personalization of an artificial pancreas; Elsevier; Expert Systems with Applications; 42; 4; 10-2014; 2234-2255
0957-4174
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/doi/10.1016/j.eswa.2014.10.038
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S0957417414006629
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 Elsevier
publisher.none.fl_str_mv Elsevier
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 13.22299

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