Computer methods and programs in biomedicine · 2026
An advanced hybrid automated insulin delivery system for insulin-dependent diabetes based on successive linearization model predictive control: The UniBE system.
СтатьяНазвание и аннотация: Перевод пока не подготовлен
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Naik Vihangkumar V, Manzoni Eleonora, Escorihuela-Altaba Clara, Garcia-Tirado Jose
Аннотация
BACKGROUND AND OBJECTIVE: Hybrid automated insulin delivery (hAID) systems represent the most advanced therapy for type 1 diabetes (T1D). Current systems rely on linear or linearized models of glucose homeostasis, which may compromise prediction accuracy and, in turn, timely decision-making by the controller. Physiological variability further complicates insulin requirements, underscoring the need for controllers that adapt dynamically and reduce user burden.
METHODS: We introduce the University of Bern (UniBE) hAID system, a framework based on successive linearization model predictive control (MPC). The controller integrates basal insulin infusion with the insulin bolus delivery module for meal-related and corrective bolus dosing, adapting bounds in real time to glucose dynamics while accounting for both automated and user-initiated inputs. In-silico evaluation was conducted using the commercial version of the FDA-accepted UVa/Padova metabolic simulator across nine scenarios involving persistent and time-varying errors in meal timing, carbohydrate estimation, and basal insulin profiles.
RESULTS: In the baseline scenario, UniBE achieved a median [interquartile ranges] time in range of 97.3 [90.8 99.1]%, with time below range at 0 [0 0.3]% and time above range at 2.4 [0.9 9.2]%. Across perturbation scenarios, median time in range remained between 81.7%-95.0%, with low hypoglycemia incidence, demonstrating resilience to clinically relevant disturbances.
CONCLUSION: The UniBE hAID system maintained stable glycemic regulation across diverse error conditions, highlighting its robustness and adaptability. These findings establish proof-of-concept feasibility and provide a foundation for further refinement, although these do not constitute a full pre-clinical validation. Future work must extend beyond in-silico testing to include more preclinical and clinical studies to rigorously assess safety, efficacy, and real-world performance.
METHODS: We introduce the University of Bern (UniBE) hAID system, a framework based on successive linearization model predictive control (MPC). The controller integrates basal insulin infusion with the insulin bolus delivery module for meal-related and corrective bolus dosing, adapting bounds in real time to glucose dynamics while accounting for both automated and user-initiated inputs. In-silico evaluation was conducted using the commercial version of the FDA-accepted UVa/Padova metabolic simulator across nine scenarios involving persistent and time-varying errors in meal timing, carbohydrate estimation, and basal insulin profiles.
RESULTS: In the baseline scenario, UniBE achieved a median [interquartile ranges] time in range of 97.3 [90.8 99.1]%, with time below range at 0 [0 0.3]% and time above range at 2.4 [0.9 9.2]%. Across perturbation scenarios, median time in range remained between 81.7%-95.0%, with low hypoglycemia incidence, demonstrating resilience to clinically relevant disturbances.
CONCLUSION: The UniBE hAID system maintained stable glycemic regulation across diverse error conditions, highlighting its robustness and adaptability. These findings establish proof-of-concept feasibility and provide a foundation for further refinement, although these do not constitute a full pre-clinical validation. Future work must extend beyond in-silico testing to include more preclinical and clinical studies to rigorously assess safety, efficacy, and real-world performance.