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dc.contributor.author
de Paula, Mariano
dc.contributor.author
Avila, Luis Omar
dc.contributor.author
Martinez, Ernesto Carlos
dc.date.available
2016-08-03T21:01:22Z
dc.date.issued
2015-06
dc.identifier.citation
de Paula, Mariano; Avila, Luis Omar; Martinez, Ernesto Carlos; Controlling blood glucose variability under uncertainty using reinforcement learning and Gaussian processes; Elsevier; Applied Soft Computing; 35; 6-2015; 310-332
dc.identifier.issn
1568-4946
dc.identifier.uri
http://hdl.handle.net/11336/6897
dc.description.abstract
Automated control of blood glucose (BG) concentration with a fully automated artificial pancreas will certainly improve the quality of life for insulin-dependent patients. Closed-loop insulin delivery is challenging due to inter- and intra-patient variability, errors in glucose sensors and delays in insulin absorption. Responding to the varying activity levels seen in outpatients, with unpredictable and unreported food intake, and providing the necessary personalized control for individuals is a challenging task for existing control algorithms. A novel approach for controlling glycemic variability using simulation-based learning is presented. A policy iteration algorithm that combines reinforcement learning with Gaussian process approximation is proposed. To account for multiple sources of uncertainty, a control policy is learned off-line using an Ito´s stochastic model of the glucose-insulin dynamics. For safety and performance, only relevant data are sampled through Bayesian active learning. Results obtained demonstrate that a generic policy is both safe and efficient for controlling subject-specific variability due to a patient´s lifestyle and its distinctive metabolic response.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-nd/2.5/ar/
dc.subject
Artificial Pancreas
dc.subject
Diabetes
dc.subject
Gaussian Processes
dc.subject
Policy Iteration
dc.subject
Reinforcement Learning
dc.subject
Stochastic Optimal Control
dc.subject.classification
Ciencias de la Computación
dc.subject.classification
Ciencias de la Computación e Información
dc.subject.classification
CIENCIAS NATURALES Y EXACTAS
dc.subject.classification
Biotecnología relacionada con la Salud
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Biotecnología de la Salud
dc.subject.classification
CIENCIAS MÉDICAS Y DE LA SALUD
dc.title
Controlling blood glucose variability under uncertainty using reinforcement learning and Gaussian processes
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2016-08-01T18:37:51Z
dc.journal.volume
35
dc.journal.pagination
310-332
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: de Paula, Mariano. Universidad Nacional del Centro de la Provincia de Buenos Aires. Facultad de Ingeniería Olavarria. Departamento de Electromecánica. Grupo INTELYMEC; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Tandil. Centro de Investigaciones en Física e Ingeniería del Centro de la Provincia de Buenos Aires; Argentina
dc.description.fil
Fil: Avila, Luis Omar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina
dc.description.fil
Fil: Martinez, Ernesto Carlos. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina
dc.journal.title
Applied Soft Computing
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/http://www.sciencedirect.com/science/article/pii/S1568494615003932
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1016/j.asoc.2015.06.041
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/10.1016/j.asoc.2015.06.041
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