Automatic glycemic regulation for the pediatric population based on switched control and time-varying IOB constraints: an in silico study.
Emilia FushimiMaría Cecilia SerafiniHernán De BattistaFabricio GarelliPublished in: Medical & biological engineering & computing (2020)
Artificial pancreas (AP) systems have shown to improve glucose regulation in type 1 diabetes (T1D) patients. However, full closed-loop performance remains a challenge particularly in children and adolescents, since these age groups often present the worst glycemic control. In this work, an algorithm based on switched control and time-varying IOB constraints is presented. The proposed control strategy is evaluated in silico using the FDA-approved UVA/ Padova simulator and its performance contrasted with the previously introduced Automatic Regulation of Glucose (ARG) algorithm in the pediatric population. The effect of unannounced meals is also explored. Results indicate that the proposed strategy achieves lower hypo- and hyperglycemia than the ARG for both announced and unannounced meals. Graphical Abstract Block diagram and illustrative example of insulin and glucose evolution over time for the proposed algorithm (ARGAE).
Keyphrases
- glycemic control
- type diabetes
- blood glucose
- deep learning
- machine learning
- neural network
- end stage renal disease
- weight loss
- newly diagnosed
- insulin resistance
- molecular docking
- cardiovascular disease
- chronic kidney disease
- ejection fraction
- prognostic factors
- oxidative stress
- patient reported
- molecular dynamics simulations
- virtual reality