Personalización automática de estrategias de control glucémico para pacientes con Diabetes Mellitus tipo 1.

ilustraciones

Autores:: Sereno Mesa, Juan Esteban

Tipo de recurso:

Fecha de publicación:: 2021

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: spa

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repository_id_str
dc.title.spa.fl_str_mv	Personalización automática de estrategias de control glucémico para pacientes con Diabetes Mellitus tipo 1.
dc.title.translated.eng.fl_str_mv	Automatic personalization of glycemic control strategies for patients with type 1 Diabetes Mellitus.
title	Personalización automática de estrategias de control glucémico para pacientes con Diabetes Mellitus tipo 1.
spellingShingle	Personalización automática de estrategias de control glucémico para pacientes con Diabetes Mellitus tipo 1. 610 - Medicina y salud::616 - Enfermedades 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Diabetes Páncreas artificial Sistemas impulsivos Control predictivo Autosintonía Detección de comidas Reconstrucción de señales Artificial pancreas Impulsive systems Predictive control Auto-tuning Meal detection Meal estimation Signal reconstruction
title_short	Personalización automática de estrategias de control glucémico para pacientes con Diabetes Mellitus tipo 1.
title_full	Personalización automática de estrategias de control glucémico para pacientes con Diabetes Mellitus tipo 1.
title_fullStr	Personalización automática de estrategias de control glucémico para pacientes con Diabetes Mellitus tipo 1.
title_full_unstemmed	Personalización automática de estrategias de control glucémico para pacientes con Diabetes Mellitus tipo 1.
title_sort	Personalización automática de estrategias de control glucémico para pacientes con Diabetes Mellitus tipo 1.
dc.creator.fl_str_mv	Sereno Mesa, Juan Esteban
dc.contributor.advisor.none.fl_str_mv	Rivadeneira Paz, Pablo Santiago
dc.contributor.author.none.fl_str_mv	Sereno Mesa, Juan Esteban
dc.subject.ddc.spa.fl_str_mv	610 - Medicina y salud::616 - Enfermedades 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería
topic	610 - Medicina y salud::616 - Enfermedades 620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingeniería Diabetes Páncreas artificial Sistemas impulsivos Control predictivo Autosintonía Detección de comidas Reconstrucción de señales Artificial pancreas Impulsive systems Predictive control Auto-tuning Meal detection Meal estimation Signal reconstruction
dc.subject.lemb.none.fl_str_mv	Diabetes
dc.subject.proposal.spa.fl_str_mv	Páncreas artificial Sistemas impulsivos Control predictivo Autosintonía Detección de comidas Reconstrucción de señales
dc.subject.proposal.eng.fl_str_mv	Artificial pancreas Impulsive systems Predictive control Auto-tuning Meal detection Meal estimation Signal reconstruction
description	ilustraciones
publishDate	2021
dc.date.accessioned.none.fl_str_mv	2021-08-12T14:44:19Z
dc.date.available.none.fl_str_mv	2021-08-12T14:44:19Z
dc.date.issued.none.fl_str_mv	2021-02-10
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/79925
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
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url	https://repositorio.unal.edu.co/handle/unal/79925 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	spa
language	spa
dc.relation.references.spa.fl_str_mv	ABDI, H. & MOLIN, P. (2007). Lilliefors/van soest’s test of normality. Encyclopedia of measurement and statistics , 540–544. AHMAD, I., MUNIR, F. & MUNIR, M. F. (2019). An adaptive backstepping based nonlinear controller for artificial pancreas in type 1 diabetes patients. Biomedical Signal Processing and Control 47, 49–56. ASCHNER, P. (2010). Epidemiología de la diabetes en Colombia. Av. en Diabetol. 26(1), 95–100. BARNES, A. J. & JONES, R. W. (2019). Pid-based glucose control using intra-peritoneal insulin infusion: An in silico study. In: 2019 14th IEEE Conference on IndustrialElectronics and Applications (ICIEA). IEEE. BENZIAN, S., REBAI, A. & AMEUR, A. (2019). Optimal digital pid controller design for regulating blood glucose level of type-i diabetic patients. International Journal of Digital Signals and Smart Systems 3(1-3), 137–149. BERGMAN et al. (1979). Quantitative estimation of insulin sensitivity 236(6), E667. BOLIE, V. W. (1961). Coefficients of normal blood glucose regulation. Journal of applied physiology 16(5), 783–788. CASAS, L. ´A. (2019). Diabetes, detección temprana para una enfermedad compleja. Revista Colombiana de Endocrinología, Diabetes & Metabolismo 6(1), 4–4. CHAKRABARTY, A., HEALEY, E., SHI, D., ZAVITSANOU, S., DOYLE, F. J. & DASSAU, E. (2019). Embedded model predictive control for a wearable artificial pancreas. IEEE Transactions on Control Systems Technology . CHEE, F., FERNANDO, T. L., SAVKIN, A. V. & VAN HEEDEN, V. (2003). Expert pid control system for blood glucose control in critically ill patients. IEEE Transactions on Information Technology in Biomedicine 7(4), 419–425. DEL FAVERO, S., TOFFANIN, C., MAGNI, L. & COBELLI, C. (2019). Deployment of modular mpc for type 1 diabetes control: the italian experience 2008–2016. In: The Artificial Pancreas. Elsevier, pp. 153–182. FARINA, L. & RINALDI, S. (2011). Positive linear systems: theory and applications, vol. 50. John Wiley & Sons. FEDERATION, I. D. (2019). IDF Diabetes Atlas, Ninth edition. Retrieved from https:// diabetesatlas.org/en/. FORLENZA, G. P., BUCKINGHAM, B. & MAAHS, D. M. (2016). Progress in diabetes technology: developments in insulin pumps, continuous glucose monitors, and progress towards the artificial pancreas. The Journal of pediatrics 169, 13–20. GAETANO, A. D. et al. (2005). Distributed-delay models of the glucose-insulin homeostasis and asymptotic state observation. IFAC Proceedings Volumes 38(1), 1041 – 1046. GARCIA-TIRADO, J., CORBETT, J. P., BOIROUX, D., JØRGENSEN, J. B. & BRETON, M. D. (2019). Closed-loop control with unannounced exercise for adults with type 1 diabetes using the ensemble model predictive control. Journal of Process Control 80, 202–210. GARCIA-TIRADO, J., ZULUAGA-BEDOYA, C. & BRETON, M. D. (2018). Identifiability analysis of three control-oriented models for use in artificial pancreas systems. Journal of diabetes science and technology 12(5), 937–952. GODOY, J., SERENO, J. E. & RIVADENEIRA, P. S. (2021). Meal detection and carbohydrate estimation based on a feedback scheme with application to the artificial pancreas. Biomedical Signal Processing and Control , 0000–0000. GOMEZ, A. M., SANCHEZ, A. M., MUNOZ, O. M. & PE˜NA, C. A. C. (2015). Numerical and clinical precision of continuous glucose monitoring in Colombian patients treated with insulin infusion pump with automated suspension in hypoglycaemia. Endocrinología y Nutrición (English Edition) 62(10), 485–492. GONZALEZ, A. H. & ODLOAK, D. (2009). A stable mpc with zone control. Journal of Process Control 19(1), 110–122. GONZ´A LEZ, A. H., RIVADENEIRA, P. S., FERRAMOSCA, A., MAGDELAINE, N. & MOOG, C. H. (2017). Impulsive zone mpc for type i diabetic patients based on a long-term model. IFAC-PapersOnLine 50(1), 14729–14734. GROSMAN, B., DASSAU, E., ZISSER, H. C., JOVANOVIˇC, L. & DOYLE III, F. J. (2010). Zone model predictive control: a strategy to minimize hyper-and hypoglycemic events. Journal of diabetes science and technology 4(4), 961–975. HEISE, T., PIEBER, T. R., DANNE, T., ERICHSEN, L. & HAAHR, H. (2017). A pooled analysis of clinical pharmacology trials investigating the pharmacokinetic and pharmacodynamic characteristics of fast-acting insulin aspart in adults with type 1 diabetes. Clinical pharmacokinetics 56(5), 551–559. HOYOS, J. D. et al. (2018). Population-based incremental learning algorithm for identification of blood glucose dynamics model for type-1 diabetic patients. In proceedings of The 2018 World Congress in Computer Science, Computer Engineering, and Applied Computing . KULCU, E., TAMADA, J. A., REACH, G., POTTS, R. O. & LESHO, M. J. (2003). Physiological differences between interstitial glucose and blood glucose measured in human subjects. Diabetes care 26(8), 2405–2409. MACIEJOWSKI, J. M. (2002). Predictive control: with constraints. Pearson education. MAGDELAINE et al. (2015). A long-term model of the glucose-insulin dynamics of type 1 diabetes. IEEE Transactions on Biomedical Engineering 62(6), 1546–1552. MAGNI, L., FORGIONE, M., TOFFANIN, C., DALLA MAN, C., KOVATCHEV, B., DE NICOLAO, G. & COBELLI, C. (2009). Run-to-run tuning of model predictive control for type 1 diabetes subjects: In silico trial. Journal of Diabetes Science and Technology 3(5), 1091–1098. MESSORI, M., TOFFANIN, C., DEL FAVERO, S., DE NICOLAO, G., COBELLI, C. & MAGNI, L. (2016). Model individualization for artificial pancreas. Computer methods and programs in biomedicine . MOHAMMADRIDHA, T., RIVADENEIRA, P. S., SERENO, J. E., CARDELLI, M. & MOOG, C. H. (2016). Description of the positive invariant sets of a type 1 diabetic patient model. XVII CLCA Latin American Conference of Automatic Control. NELDER, J. A. & MEAD, R. (1965). A simplex method for function minimization. The computer journal 7(4), 308–313. PERRIELLO, G., DE FEO, P., TORLONE, E., FANELLI, C., SANTEUSANIO, F., BRUNETTI, P. & BOLLI, G. (1991). The dawn phenomenon in type 1 (insulin-dependent) diabetes mellitus: magnitude, frequency, variability, and dependency on glucose counter-regulation and insulin sensitivity. Diabetologia 34(1), 21–28. PINSKER, J. E., LEE, J. B., DASSAU, E., SEBORG, D. E., BRADLEY, P. K., GONDHALEKAR, R., BEVIER, W. C., HUYETT, L., ZISSER, H. C. & DOYLE, F. J. (2016). Randomized crossover comparison of personalized mpc and pid control algorithms for the artificial pancreas. Diabetes Care 39(7), 1135–1142. RESALAT, N., EL YOUSSEF, J., REDDY, R. & JACOBS, P. G. (2017). Evaluation of model complexity in model predictive control within an exercise-enabled artificial pancreas. IFAC-PapersOnLine 50(1), 7756–7761. RIVADENEIRA, P. S., CAICEDO, M. A. & SERENO, J. E. (2018). A new approach in zone model predictive control for type 1 diabetes to be tested in Colombia. 11th International Conference on Advanced Technologies & Treatments for Diabetes (ATTD 2018) 20, 86–A87. RIVADENEIRA, P. S., FERRAMOSCA, A. & GONZALEZ, A. H. (2016). Control algorithms for non-zero set-point regulation of linear impulsive systems. In: submitted to IEEE transactions to Automatic Control. RIVADENEIRA, P. S., GODOY, J., SERENO, J., ABUIN, P., FERRAMOSCA, A. & GONZALEZ, A. (2020). Impulsive mpc schemes for biomedical processes: Application to type 1 diabetes. In: Control Applications for Biomedical Engineering Systems. Elsevier, pp. 55–87. RIVADENEIRA, P. S., SERENO, J. E. & CAICEDO, M. A. (2019). Nuevas estrategias de control glucémico en pacientes con diabetes mellitus tipo 1. Revista Iberoamericana de Automática e Informática. 16(2), 238–248. RUAN, Y., WILINSKA, M. E., THABIT, H. & HOVORKA, R. (2016). Modelling day-to-day variability of glucose–insulin regulation over 12-week home use of closed-loop insulin delivery. IEEE Transactions on Biomedical Engineering 64(6), 1412–1419. SCHALLER, H., SCHAUPP, L., BODENLENZ, M., WILINSKA, M., CHASSIN, L., WACH, P., VERING, T., HOVORKA, R. & PIEBER, T. (2006). On-line adaptive algorithm with glucose prediction capacity for subcutaneous closed loop control of glucose: evaluation under fasting conditions in patients with type 1 diabetes. Diabetic medicine 23(1), 90–93. SERENO, J. E., CAICEDO, M. A. & RIVADENEIRA, P. S. (2018a). Artificial pancreas: glycemic control strategies for avoiding hypoglycemia. Dyna. 85(207), 198–207. SERENO, J. E., CAICEDO, M. A., RIVADENEIRA, P. S. & CAMACHO, O. E. (2018b). In silico test for mpc and smc controllers under parametric variations in type 1 diabetic patients. 2018 Argentine Conference on Automatic Control (AADECA) , 1–6. SERENO, J. E. & RIVADENEIRA, P. S. (2018). Auto-tuning for model predictive controllers in patients with type 1 diabetes. 2018 Argentine Conference on Automatic Control (AADECA) , 1–6. THABIT, H. & HOVORKA, R. (2016). Coming of age: the artificial pancreas for type 1 diabetes. Diabetologia 59(9), 1795–1805. TOFFANIN, C., AIELLO, E., DEL FAVERO, S., COBELLI, C. & MAGNI, L. (2019). Multiple models for artificial pancreas predictions identified from free-living condition data: A proof of concept study. Journal of Process Control 77, 29–37. WALSH, J., ROBERTS, R. & HEINEMANN, L. (2014). Confusion regarding duration of insulin action: a potential source for major insulin dose errors by bolus calculators. Journal of diabetes science and technology 8(1), 170–178. WONG, J. M. & JENKINS, D. J. (2007). Carbohydrate digestibility and metabolic effects. The Journal of nutrition 137(11), 2539S–2546S. ZARKOGIANNI, K., VAZEOU, A., MOUGIAKAKOU, S. G., PROUNTZOU, A. & NIKITA, K. S. (2011). An insulin infusion advisory system based on autotuning nonlinear model-predictive control. IEEE Transactions on Biomedical Engineering 58(9), 2467–2477.
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dc.format.extent.spa.fl_str_mv	76 páginas
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dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Minas - Maestría en Ingeniería - Automatización Industrial
dc.publisher.department.spa.fl_str_mv	Departamento de Ingeniería Eléctrica y Automática
dc.publisher.faculty.spa.fl_str_mv	Facultad de Minas
dc.publisher.place.spa.fl_str_mv	Medellín
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-SinDerivadas 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Rivadeneira Paz, Pablo Santiago04730d3951ae0b8d3cad652e1130f609600Sereno Mesa, Juan Esteban7765209d75c4578f3ab1286c7ed4dd1f2021-08-12T14:44:19Z2021-08-12T14:44:19Z2021-02-10https://repositorio.unal.edu.co/handle/unal/79925Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustracionesEl páncreas artificial se ha consolidado como nuevo tratamiento para las personas con diabetes mellitus tipo 1. Esta enfermedad crónica y autoinmune deteriora la regulación glucémica en los pacientes que la padecen y se presenta como un problema de salud pública mundial en crecimiento. En la actualidad diversas pruebas clínicas a nivel mundial han demostrado la validez de los algoritmos de control glucémico. No obstante, nuevos retos se presentan para la concreción de un sistema totalmente automatizado, entre ellos la sintonización automática, la personalización y la eliminación del anuncio de comidas. En el presente trabajo se desarrollan los siguientes temas: Primero, se proponen dos algoritmos de control glucémico con rechazo de perturbaciones de comidas no anunciadas: el primero es una estrategia en paralelo entre un controlador por realimentación y un PID positivo (K+PID); el segundo es un controlador predictivo por zonas con entrada impulsiva (iZMPC). Los algoritmos son validados en un total de 50 pacientes virtuales. Los resultados muestran que ambos controladores logran evitar los casos de hipoglucemia y mantener los niveles de glucosa en la zona de normoglucemia (98, 13% y 95, 01% del tiempo para el iZMPC y el K+PID, respectivamente) ante perturbaciones de comidas no anunciadas. Luego, se propone una metodología para la sintonización automática de controladores glucémicos. Dicha propuesta se basa en un procedimiento de optimización de los parámetros de sintonía haciendo uso del método Nelder-Mead, para maximizar el tiempo de permanencia en normoglucemia. La validación se realiza con 33 pacientes virtuales extraídos del simulador virtual UVa/Padova y con el controlador iZMPC. Los resultados obtenidos muestran un incremento promedio de 17, 21% más de tiempo en normoglucemia, con respecto a la sintonización inicial. Finalmente, se propone un algoritmo de detección y estimación de comidas para la reconstrucción de señales de carbohidratos. El algoritmo propone un esquema de realimentación y un estimador en funciones de transferencia para la detección y estimación de comidas, en base a la señal entregada por el estimador se realiza la reconstrucción de la señal de anuncio de comidas. La validación se realiza por medio de datos recolectados en 30 pacientes virtuales y 5 reales. Los resultados muestran que en promedio el algoritmo presenta una sensibilidad de 98 %, un error de estimación del 14% en la amplitud de la comida y un desfase temporal de 4 min con respecto al inicio real de la comida. (Tomado de la fuente)Artificial pancreas has established as a new treatment for people with type 1 diabetes mellitus. This chronic and autoimmune illness impairs the glycaemic regulation in patients who suffer it and presents itself as a growing global public health problem. Currently, worldwide clinical trials have demonstrated the validity of glycaemic control algorithms. However, new challenges arise for the realization of a fully automated system, including automatic tuning, personalization and the elimination of meals announcements. In this work the following topics are developed: Firstly, two glycaemic control algorithms with rejection of unannounced meals are proposed: the first is a parallel strategy between a feedback controller and a positive PID controller (K+PID); the second one is a zone model predictive control with impulsive input (iZMPC). The algorithms are validated in a total of 50 virtual patients. The results show that both controllers manage to avoid cases of hypoglycemia and maintain glucose levels in the normoglycemia zone (98,13% and 95,01% of the time for the iZMPC and the K + PID, respectively) in face of disturbances of unannounced meals. Then, a methodology for automatic tuning of glycaemic controllers is proposed. This approach is based on a procedure of optimizing the controller parameters using the Nelder-Mead method to maximize the time spent in normoglycemia. Validation is performed with 33 virtual patients extracted from the UVa/Padova virtual simulator an with the iZMPC controller. Simulation results show an average increase of 17,21% more time in normoglycemia, with respect to the initial tuning. Finally, a meal detection and estimation algorithm is proposed for reconstruction for carbohydrate signals. The algorithm proposes a feedback scheme and an estimator, in transfer functions, for the detection and estimation of meals. Based on the estimator output signal, the reconstruction of the meal announcement signal is performed. Validation is performed using data collected from 30 virtual patients and 5 real patients. The results show that on average the algorithm presents a sensitivity of 98 %, an estimation error of 14% in the meal size, and a time lag of 4 min with respect to the actual meal onset. (Tomado de la fuente)MaestríaMagíster en Ingeniería - Automatización Industrial76 páginasapplication/pdfspaUniversidad Nacional de ColombiaMedellín - Minas - Maestría en Ingeniería - Automatización IndustrialDepartamento de Ingeniería Eléctrica y AutomáticaFacultad de MinasMedellínUniversidad Nacional de Colombia - Sede Medellín610 - Medicina y salud::616 - Enfermedades620 - Ingeniería y operaciones afines::629 - Otras ramas de la ingenieríaDiabetesPáncreas artificialSistemas impulsivosControl predictivoAutosintoníaDetección de comidasReconstrucción de señalesArtificial pancreasImpulsive systemsPredictive controlAuto-tuningMeal detectionMeal estimationSignal reconstructionPersonalización automática de estrategias de control glucémico para pacientes con Diabetes Mellitus tipo 1.Automatic personalization of glycemic control strategies for patients with type 1 Diabetes Mellitus.Trabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMABDI, H. & MOLIN, P. (2007). Lilliefors/van soest’s test of normality. Encyclopedia of measurement and statistics , 540–544.AHMAD, I., MUNIR, F. & MUNIR, M. F. (2019). An adaptive backstepping based nonlinear controller for artificial pancreas in type 1 diabetes patients. Biomedical Signal Processing and Control 47, 49–56.ASCHNER, P. (2010). Epidemiología de la diabetes en Colombia. Av. en Diabetol. 26(1), 95–100.BARNES, A. J. & JONES, R. W. (2019). Pid-based glucose control using intra-peritoneal insulin infusion: An in silico study. In: 2019 14th IEEE Conference on IndustrialElectronics and Applications (ICIEA). IEEE.BENZIAN, S., REBAI, A. & AMEUR, A. (2019). Optimal digital pid controller design for regulating blood glucose level of type-i diabetic patients. International Journal of Digital Signals and Smart Systems 3(1-3), 137–149.BERGMAN et al. (1979). Quantitative estimation of insulin sensitivity 236(6), E667.BOLIE, V. W. (1961). Coefficients of normal blood glucose regulation. Journal of applied physiology 16(5), 783–788.CASAS, L. ´A. (2019). Diabetes, detección temprana para una enfermedad compleja. Revista Colombiana de Endocrinología, Diabetes & Metabolismo 6(1), 4–4.CHAKRABARTY, A., HEALEY, E., SHI, D., ZAVITSANOU, S., DOYLE, F. J. & DASSAU, E. (2019). Embedded model predictive control for a wearable artificial pancreas. IEEE Transactions on Control Systems Technology .CHEE, F., FERNANDO, T. L., SAVKIN, A. V. & VAN HEEDEN, V. (2003). Expert pid control system for blood glucose control in critically ill patients. IEEE Transactions on Information Technology in Biomedicine 7(4), 419–425.DEL FAVERO, S., TOFFANIN, C., MAGNI, L. & COBELLI, C. (2019). Deployment of modular mpc for type 1 diabetes control: the italian experience 2008–2016. In: The Artificial Pancreas. Elsevier, pp. 153–182.FARINA, L. & RINALDI, S. (2011). Positive linear systems: theory and applications, vol. 50. John Wiley & Sons.FEDERATION, I. D. (2019). IDF Diabetes Atlas, Ninth edition. Retrieved from https:// diabetesatlas.org/en/.FORLENZA, G. P., BUCKINGHAM, B. & MAAHS, D. M. (2016). Progress in diabetes technology: developments in insulin pumps, continuous glucose monitors, and progress towards the artificial pancreas. The Journal of pediatrics 169, 13–20.GAETANO, A. D. et al. (2005). Distributed-delay models of the glucose-insulin homeostasis and asymptotic state observation. IFAC Proceedings Volumes 38(1), 1041 – 1046.GARCIA-TIRADO, J., CORBETT, J. P., BOIROUX, D., JØRGENSEN, J. B. & BRETON, M. D. (2019). Closed-loop control with unannounced exercise for adults with type 1 diabetes using the ensemble model predictive control. Journal of Process Control 80, 202–210.GARCIA-TIRADO, J., ZULUAGA-BEDOYA, C. & BRETON, M. D. (2018). Identifiability analysis of three control-oriented models for use in artificial pancreas systems. Journal of diabetes science and technology 12(5), 937–952.GODOY, J., SERENO, J. E. & RIVADENEIRA, P. S. (2021). Meal detection and carbohydrate estimation based on a feedback scheme with application to the artificial pancreas. Biomedical Signal Processing and Control , 0000–0000.GOMEZ, A. M., SANCHEZ, A. M., MUNOZ, O. M. & PE˜NA, C. A. C. (2015). Numerical and clinical precision of continuous glucose monitoring in Colombian patients treated with insulin infusion pump with automated suspension in hypoglycaemia. Endocrinología y Nutrición (English Edition) 62(10), 485–492.GONZALEZ, A. H. & ODLOAK, D. (2009). A stable mpc with zone control. Journal of Process Control 19(1), 110–122.GONZ´A LEZ, A. H., RIVADENEIRA, P. S., FERRAMOSCA, A., MAGDELAINE, N. & MOOG, C. H. (2017). Impulsive zone mpc for type i diabetic patients based on a long-term model. IFAC-PapersOnLine 50(1), 14729–14734.GROSMAN, B., DASSAU, E., ZISSER, H. C., JOVANOVIˇC, L. & DOYLE III, F. J. (2010). Zone model predictive control: a strategy to minimize hyper-and hypoglycemic events. 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Personalización automática de estrategias de control glucémico para pacientes con Diabetes Mellitus tipo 1.

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