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An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems.
Ahmad, Sayyar; Beneyto, Aleix; Zhu, Taiyu; Contreras, Ivan; Georgiou, Pantelis; Vehi, Josep.
Afiliación
  • Ahmad S; Modeling and Intelligent Control Engineering Laboratory, Institute of Informatics and Applications, University of Girona, 17003, Girona, Spain.
  • Beneyto A; Modeling and Intelligent Control Engineering Laboratory, Institute of Informatics and Applications, University of Girona, 17003, Girona, Spain.
  • Zhu T; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK.
  • Contreras I; Modeling and Intelligent Control Engineering Laboratory, Institute of Informatics and Applications, University of Girona, 17003, Girona, Spain.
  • Georgiou P; Centre for Bio-Inspired Technology, Department of Electrical and Electronic Engineering, Imperial College London, London, UK.
  • Vehi J; Modeling and Intelligent Control Engineering Laboratory, Institute of Informatics and Applications, University of Girona, 17003, Girona, Spain. josep.vehi@udg.edu.
Sci Rep ; 14(1): 15245, 2024 07 02.
Article en En | MEDLINE | ID: mdl-38956183
ABSTRACT
In hybrid automatic insulin delivery (HAID) systems, meal disturbance is compensated by feedforward control, which requires the announcement of the meal by the patient with type 1 diabetes (DM1) to achieve the desired glycemic control performance. The calculation of insulin bolus in the HAID system is based on the amount of carbohydrates (CHO) in the meal and patient-specific parameters, i.e. carbohydrate-to-insulin ratio (CR) and insulin sensitivity-related correction factor (CF). The estimation of CHO in a meal is prone to errors and is burdensome for patients. This study proposes a fully automatic insulin delivery (FAID) system that eliminates patient intervention by compensating for unannounced meals. This study exploits the deep reinforcement learning (DRL) algorithm to calculate insulin bolus for unannounced meals without utilizing the information on CHO content. The DRL bolus calculator is integrated with a closed-loop controller and a meal detector (both previously developed by our group) to implement the FAID system. An adult cohort of 68 virtual patients based on the modified UVa/Padova simulator was used for in-silico trials. The percentage of the overall duration spent in the target range of 70-180 mg/dL was 71.2 % and 76.2 % , < 70 mg/dL was 0.9 % and 0.1 % , and > 180 mg/dL was 26.7 % and 21.1 % , respectively, for the FAID system and HAID system utilizing a standard bolus calculator (SBC) including CHO misestimation. The proposed algorithm can be exploited to realize FAID systems in the future.
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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sistemas de Infusión de Insulina / Diabetes Mellitus Tipo 1 / Aprendizaje Profundo / Insulina Límite: Adult / Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: España

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Asunto principal: Sistemas de Infusión de Insulina / Diabetes Mellitus Tipo 1 / Aprendizaje Profundo / Insulina Límite: Adult / Humans Idioma: En Revista: Sci Rep Año: 2024 Tipo del documento: Article País de afiliación: España
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