Nonlinear model predictive control of glucose concentration in subjects with type 1 diabetes.

By R. Hovorka, V. Canonico, L. Chassin, U. Haueter, M. Massi-Benedetti, M. Orsini Federici, T. Pieber, H. Schaller, L. Schaupp, T. Vering and 1 more. 2004

1,293 citations recorded by the source corpus.

Subfields: Control and Systems Engineering, Endocrinology, Diabetes and Metabolism, Surgery

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Citations in the corpus

Reinforcement Learning in Healthcare: A Survey (2019, methodology): “applied model-based VI method [123] and AC method [124] to reduce the fluctuation of the blood glucose in both fasting and post-meal scenarios, drawing on the Bergman’s minimal insulin-glucose kinetics model [125] and the Hovorka model [126] to simulate a patient.”
The future of open‐ and closed‐loop insulin delivery systems (2008, methodology): “More recently, control engineers have gained an interest in the pharmacokinetic model developed by Hovorka et al (2002, 2004).”
Challenges and Research Directions in Medical Cyber–Physical Systems (2012, methodology): “Closed-loop BG Control: We have implemented the glucose-insulin patient model introduced in [55] in Simulink.”
A Critical Assessment of Algorithms and Challenges in the Development of a Closed-Loop Artificial Pancreas (2005, positive): “36,37 Preliminary development of the MPC-based system was based on simulation studies, while initial clinical closed-loop studies used an i.v. sensor with a 15-min sample time and s.c. delivery of insulin (i.v.-s.c.).”
Meal Simulation Model of the Glucose-Insulin System (2007, methodology): “A new model of glucose transit through the gastro-intestinal tract is used to describe glucose ingestion and absorption [17]; this feature is important because previous simulation models either allowed only intravenous glucose administration [3], [4] or described the process simplistically [8], [11]”
Artificial Pancreas: Past, Present, Future (2011, background): “The 3-year ADICOL project funded by the European Commission showed the feasibility of using advanced MPC strategies to close the loop (26).”
On-line policy learning and adaptation for real-time personalization of an artificial pancreas (2015, methodology): “In the work of Magni et al. (2007), a linear MPC controller was assessed whereas in Magni et al. (2009) the trial was extended to evaluate a nonlinear state feedback MPC controller (Hovorka et al., 2004).”