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...
- Autores:
-
Rios, Y. Yuliana
García-Rodríguez, J.A
Sanchez, Edgar N.
Alanis, Alma Y.
Ruiz-Velázquez, E.
Pardo Garcia, Aldo
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12172
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12172
- Palabra clave:
- Recurrent neural network
Fuzzy inference
Uva/Padova simulator
Neural multi-step predictor
Type 1 Diabetes Mellitus
- Rights
- openAccess
- License
- http://creativecommons.org/licenses/by-nc-nd/4.0/
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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 Type 1 Diabetes Mellitus |
| 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, Y. Yuliana García-Rodríguez, J.A Sanchez, Edgar N. Alanis, Alma Y. Ruiz-Velázquez, E. Pardo Garcia, Aldo |
| dc.contributor.author.none.fl_str_mv |
Rios, Y. Yuliana García-Rodríguez, J.A Sanchez, Edgar N. Alanis, Alma Y. Ruiz-Velázquez, E. Pardo Garcia, Aldo |
| dc.subject.keywords.spa.fl_str_mv |
Recurrent neural network Fuzzy inference Uva/Padova simulator Neural multi-step predictor Type 1 Diabetes Mellitus |
| topic |
Recurrent neural network Fuzzy inference Uva/Padova simulator Neural multi-step predictor Type 1 Diabetes Mellitus |
| 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 |
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2022 |
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2022-07 |
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2023-07-19T21:13:05Z |
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2023-07-19T21:13:05Z |
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2023-07 |
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Yuliana Rios, Y., García-Rodríguez, J. A., Sanchez, E. N., Alanis, A. Y., Ruiz-Velázquez, E., & Garcia, A. P. (2022). Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction. ISA Transactions, 126. https://doi.org/10.1016/j.isatra.2021.07.045 |
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https://hdl.handle.net/20.500.12585/12172 |
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10.1016/j.isatra.2021.07.045 |
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Universidad Tecnológica de Bolívar |
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Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Yuliana Rios, Y., García-Rodríguez, J. A., Sanchez, E. N., Alanis, A. Y., Ruiz-Velázquez, E., & Garcia, A. P. (2022). Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction. ISA Transactions, 126. https://doi.org/10.1016/j.isatra.2021.07.045 10.1016/j.isatra.2021.07.045 Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
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https://hdl.handle.net/20.500.12585/12172 |
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eng |
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eng |
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info:eu-repo/semantics/openAccess |
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Attribution-NonCommercial-NoDerivatives 4.0 Internacional |
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10 páginas |
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ISA Transactions - Vol. 126 |
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Universidad Tecnológica de Bolívar |
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Rios, Y. Yuliana2e5d244a-ee96-4d51-8441-17659df20a44García-Rodríguez, J.A66fb6c6d-c6df-498c-9e12-85c5a06389a3Sanchez, Edgar N.dde5501b-9920-4b9c-8ae4-6378407a487cAlanis, Alma Y.e3a9155e-04ad-4ce3-972e-03923c3305beRuiz-Velázquez, E.c52110a4-da5c-4ebe-acb8-77dc239458f5Pardo Garcia, Aldo3e44cda3-c4d1-46d1-8551-af25e3f4caa72023-07-19T21:13:05Z2023-07-19T21:13:05Z2022-072023-07Yuliana Rios, Y., García-Rodríguez, J. A., Sanchez, E. N., Alanis, A. Y., Ruiz-Velázquez, E., & Garcia, A. P. (2022). Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step prediction. ISA Transactions, 126. https://doi.org/10.1016/j.isatra.2021.07.045https://hdl.handle.net/20.500.12585/1217210.1016/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 páginasPdfapplication/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 Transactions - Vol. 126Treatment for T1DM patients by a neuro-fuzzy inverse optimal controller including multi-step predictioninfo:eu-repo/semantics/articleinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_2df8fbb1Recurrent neural networkFuzzy inferenceUva/Padova simulatorNeural multi-step predictorType 1 Diabetes MellitusCartagena de IndiasCampus TecnológicoThe Effect of Intensive Treatment of Diabetes on the Development and Progression of Long-Term Complications in Insulin-Dependent Diabetes Mellitus (1993) New England Journal of Medicine, 329 (14), pp. 977-986. Cited 23013 times. doi: 10.1056/NEJM199309303291401Turner, R. Intensive blood-glucose control with sulphonylureas or insulin compared with conventional treatment and risk of complications in patients with type 2 diabetes (UKPDS 33) (1998) Lancet, 352 (9131), pp. 837-853. Cited 19212 times. http://www.journals.elsevier.com/the-lancet/ doi: 10.1016/S0140-6736(98)07019-6DeFronzo, R., Ferrannini, E., Alberti, K., Zimmet, P. The classification and diagnosis of diabetes mellitus (2015) International textbook of diabetes mellitus, pp. 24-30. Sons J.W.&. 4th ed. John Wiley & Sons New Jersey, Middle Atlantic, U.S. [Ch. 1]DeFronzo, R.A. Epidemiology and risk factors for type 1 diabetes mellitus (2015) International textbook of diabetes mellitus, pp. 17-28. Sons J.W.&. 4th ed. John Wiley & Sons Oxford, U.K. [Ch. 2]Majithia, A.R., Wiltschko, A.B., Zheng, H., Walford, G.A., Nathan, D.M. Rate of Change of Premeal Glucose Measured by Continuous Glucose Monitoring Predicts Postmeal Glycemic Excursions in Patients With Type 1 Diabetes: Implications for Therapy (2018) Journal of Diabetes Science and Technology, 12 (1), pp. 76-82. Cited 5 times. http://dst.sagepub.com/content/by/year doi: 10.1177/1932296817725756Hovorka, R. Continuous glucose monitoring and closed-loop systems (2006) Diabetic Medicine, 23 (1), pp. 1-12. Cited 357 times. doi: 10.1111/j.1464-5491.2005.01672.xKux, L. Guidance for industry and food and drug administration staff; the content of investigational device exemption and premarket approval applications for artificial pancreas device systems; availability (2012) Fed Regist, 77 (226), pp. 1-63. Cited 3 times.Messori, M., Paolo Incremona, G., Cobelli, C., Magni, L. Individualized model predictive control for the artificial pancreas: In silico evaluation of closed-loop glucose control (2018) IEEE Control Systems, 38 (1), art. no. 8263475, pp. 86-104. 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Cited 11 times. ISBN: 978-142443428-2 doi: 10.1109/ICSCCW.2009.5379497Geman, O., Chiuchisan, I., Toderean, R. Application of Adaptive Neuro-Fuzzy Inference System for diabetes classification and prediction (Open Access) (2017) 2017 E-Health and Bioengineering Conference, EHB 2017, art. no. 7995505, pp. 639-642. Cited 30 times. ISBN: 978-153860358-1 doi: 10.1109/EHB.2017.7995505Lekkas, S., Mikhailov, L. Evolving fuzzy medical diagnosis of Pima Indians diabetes and of dermatological diseases (Open Access) (2010) Artificial Intelligence in Medicine, 50 (2), pp. 117-126. Cited 76 times. doi: 10.1016/j.artmed.2010.05.007Nath, A., Dey, R., Balas, V.E. Closed loop blood glucose regulation of type 1 diabetic patient using Takagi-Sugeno fuzzy logic control (2018) Advances in Intelligent Systems and Computing, 634, pp. 286-296. Cited 10 times. http://www.springer.com/series/11156 ISBN: 978-331962523-2 doi: 10.1007/978-3-319-62524-9_23Alanis, A.Y., Sanchez, E.N., Loukianov, A.G. 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