Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction

Diabetes Mellitus is a serious metabolic condition for global health associations. Recently, the number of adults, adolescents and children who have developed Type 1 Diabetes Mellitus (T1DM) has increased as well as the mortality statistics related to this disease. For this reason, the scientific co...

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Autores:
Rios a, Y. Yuliana
García Rodríguez, J. A.
Sanchez c, Edgar N.
Alanis, Alma Y.
Tipo de recurso:
Fecha de publicación:
2021
Institución:
Universidad Tecnológica de Bolívar
Repositorio:
Repositorio Institucional UTB
Idioma:
eng
OAI Identifier:
oai:repositorio.utb.edu.co:20.500.12585/10420
Acceso en línea:
https://hdl.handle.net/20.500.12585/10420
https://doi.org/10.1016/j.isatra.2021.07.045
Palabra clave:
Recurrent neural network
Fuzzy inference
Uva/Padova simulator
Neural multi-step predictor
Diabetes Mellitus
LEMB
Rights
openAccess
License
http://creativecommons.org/licenses/by-nc-nd/4.0/
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dc.title.spa.fl_str_mv Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction
title Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction
spellingShingle Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction
Recurrent neural network
Fuzzy inference
Uva/Padova simulator
Neural multi-step predictor
Diabetes Mellitus
LEMB
title_short Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction
title_full Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction
title_fullStr Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction
title_full_unstemmed Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction
title_sort Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction
dc.creator.fl_str_mv Rios a, Y. Yuliana
García Rodríguez, J. A.
Sanchez c, Edgar N.
Alanis, Alma Y.
dc.contributor.author.none.fl_str_mv Rios a, Y. Yuliana
García Rodríguez, J. A.
Sanchez c, Edgar N.
Alanis, Alma Y.
dc.subject.keywords.spa.fl_str_mv Recurrent neural network
Fuzzy inference
Uva/Padova simulator
Neural multi-step predictor
Diabetes Mellitus
topic Recurrent neural network
Fuzzy inference
Uva/Padova simulator
Neural multi-step predictor
Diabetes Mellitus
LEMB
dc.subject.armarc.none.fl_str_mv LEMB
description Diabetes Mellitus is a serious metabolic condition for global health associations. Recently, the number of adults, adolescents and children who have developed Type 1 Diabetes Mellitus (T1DM) has increased as well as the mortality statistics related to this disease. For this reason, the scientific community has directed research in developing technologies to reduce T1DM complications. This contribution is related to a feedback control strategy for blood glucose management in population samples of ten virtual adult subjects, adolescents and children. This scheme focuses on the development of an inverse optimal control (IOC) proposal which is integrated by neural identification, a multi-step prediction (MSP) strategy, and Takagi–Sugeno (T–S) fuzzy inference to shape the convenient insulin infusion in the treatment of T1DM patients. The MSP makes it possible to estimate the glucose dynamics 15 min in advance; therefore, this estimation allows the Neuro-Fuzzy-IOC (NF-IOC) controller to react in advance to prevent hypoglycemic and hyperglycemic events. The T–S fuzzy membership functions are defined in such a way that the respective inferences change basal infusion rates for each patient’s condition. The results achieved for scenarios simulated in Uva/Padova virtual software illustrate that this proposal is suitable to maintain blood glucose levels within normoglycemic values (70–115 mg/dL); furthermore, this level remains less than 250 mg/dL during the postprandial event. A comparison between a simple neural IOC (NIOC) and the proposed NF-IOC is carried out using the analysis for control variability named CVGA chart included in the Uva/Padova software. This analysis highlights the improvement of the NF-IOC treatment, proposed in this article, on the NIOC approach because each subject is located inside safe zones for the entire duration of the simulation
publishDate 2021
dc.date.issued.none.fl_str_mv 2021-07-30
dc.date.accessioned.none.fl_str_mv 2022-01-28T20:02:15Z
dc.date.available.none.fl_str_mv 2022-01-28T20:02:15Z
dc.date.submitted.none.fl_str_mv 2022-01-27
dc.type.driver.spa.fl_str_mv info:eu-repo/semantics/article
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dc.identifier.citation.spa.fl_str_mv Y.Y. Rios, J.A. García-Rodríguez, E.N. Sanchez et al., Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction. ISA Transactions (2021), https://doi.org/10.1016/j.isatra.2021.07.045.
dc.identifier.uri.none.fl_str_mv https://hdl.handle.net/20.500.12585/10420
dc.identifier.doi.none.fl_str_mv https://doi.org/10.1016/j.isatra.2021.07.045
dc.identifier.instname.spa.fl_str_mv Universidad Tecnológica de Bolívar
dc.identifier.reponame.spa.fl_str_mv Repositorio Universidad Tecnológica de Bolívar
identifier_str_mv Y.Y. Rios, J.A. García-Rodríguez, E.N. Sanchez et al., Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction. ISA Transactions (2021), https://doi.org/10.1016/j.isatra.2021.07.045.
Universidad Tecnológica de Bolívar
Repositorio Universidad Tecnológica de Bolívar
url https://hdl.handle.net/20.500.12585/10420
https://doi.org/10.1016/j.isatra.2021.07.045
dc.language.iso.spa.fl_str_mv eng
language eng
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dc.rights.accessrights.spa.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.cc.*.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 Internacional
rights_invalid_str_mv http://creativecommons.org/licenses/by-nc-nd/4.0/
Attribution-NonCommercial-NoDerivatives 4.0 Internacional
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eu_rights_str_mv openAccess
dc.format.extent.none.fl_str_mv 10 Páginas
dc.format.mimetype.spa.fl_str_mv application/pdf
dc.publisher.place.spa.fl_str_mv Cartagena de Indias
dc.source.spa.fl_str_mv ISA Transactions
institution Universidad Tecnológica de Bolívar
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spelling Rios a, Y. Yuliana21831cb7-12a3-4d2c-9576-3040e4d0c947García Rodríguez, J. A.66fb6c6d-c6df-498c-9e12-85c5a06389a3Sanchez c, Edgar N.62704aac-d07c-4525-a914-fd558012929aAlanis, Alma Y.e3a9155e-04ad-4ce3-972e-03923c3305be2022-01-28T20:02:15Z2022-01-28T20:02:15Z2021-07-302022-01-27Y.Y. Rios, J.A. García-Rodríguez, E.N. Sanchez et al., Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction. ISA Transactions (2021), https://doi.org/10.1016/j.isatra.2021.07.045.https://hdl.handle.net/20.500.12585/10420https://doi.org/10.1016/j.isatra.2021.07.045Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarDiabetes Mellitus is a serious metabolic condition for global health associations. Recently, the number of adults, adolescents and children who have developed Type 1 Diabetes Mellitus (T1DM) has increased as well as the mortality statistics related to this disease. For this reason, the scientific community has directed research in developing technologies to reduce T1DM complications. This contribution is related to a feedback control strategy for blood glucose management in population samples of ten virtual adult subjects, adolescents and children. This scheme focuses on the development of an inverse optimal control (IOC) proposal which is integrated by neural identification, a multi-step prediction (MSP) strategy, and Takagi–Sugeno (T–S) fuzzy inference to shape the convenient insulin infusion in the treatment of T1DM patients. The MSP makes it possible to estimate the glucose dynamics 15 min in advance; therefore, this estimation allows the Neuro-Fuzzy-IOC (NF-IOC) controller to react in advance to prevent hypoglycemic and hyperglycemic events. The T–S fuzzy membership functions are defined in such a way that the respective inferences change basal infusion rates for each patient’s condition. The results achieved for scenarios simulated in Uva/Padova virtual software illustrate that this proposal is suitable to maintain blood glucose levels within normoglycemic values (70–115 mg/dL); furthermore, this level remains less than 250 mg/dL during the postprandial event. A comparison between a simple neural IOC (NIOC) and the proposed NF-IOC is carried out using the analysis for control variability named CVGA chart included in the Uva/Padova software. This analysis highlights the improvement of the NF-IOC treatment, proposed in this article, on the NIOC approach because each subject is located inside safe zones for the entire duration of the simulation10 Páginasapplication/pdfenghttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://purl.org/coar/access_right/c_abf2ISA TransactionsTreatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step predictioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/restrictedAccesshttp://purl.org/coar/resource_type/c_2df8fbb1Recurrent neural networkFuzzy inferenceUva/Padova simulatorNeural multi-step predictorDiabetes MellitusLEMBCartagena de IndiasControl TD, Group CTR. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulindependent diabetes mellitus. 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Washington, D.C., U.S.: Elsevier; 2005, p. 1319–31. http://dx.doi.org/10.17226/10490, [Ch. Summary Ta]. [45] Previato* HDRdA. Carbohydrate counting in diabetes. Nutr Food TechnolAmerican Diabetes Association PS. Nutrition recommendations and interventions for diabetes - a position statement. Diabetes Care 2008;31(1):S61–78. http://dx.doi.org/10.2337/dc08-S061.Association AD, et al. All about carbohydrate counting. American Diabetes Association; 2009, URL https://professional.diabetes.org/sites/professional. diabetes.org/files/media/All_About_Carbohydrate_Counting.pdfCinar A. Artificial pancreas systems: An introduction to the special issue. 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