This work introduces a novel zone model predictive control (MPC) based on Gaussian Process models (GPs) for the artificial pancreas (AP). The main novelty of the proposal is to exploit a GP that is trained on previously collected metabolic data of type 1 diabetes mellitus (T1DM) patients, to regulate the blood glucose levels by means of a personalized MPC strategy that automatically adjusts the basal insulin and the insulin boluses to be injected to the patients. The average closed-loop performance is improved in terms of classical indexes such as time in range, avoidance of critic hypoglycaemia episodes and avoidance of long-term hyperglycaemia events. The controller was evaluated in-silico by means of the FDA-accepted UVA/Padova metabolic simulator on 11 adult T1DM patients, showing promising results.

(2023). Artificial Pancreas under a Zone Model Predictive Control based on Gaussian Process models: toward the personalization of the closed loop . Retrieved from https://hdl.handle.net/10446/260097

Artificial Pancreas under a Zone Model Predictive Control based on Gaussian Process models: toward the personalization of the closed loop

Polver, Marco;Sonzogni, Beatrice;Mazzoleni, Mirko;Previdi, Fabio;Ferramosca, Antonio
2023-01-01

Abstract

This work introduces a novel zone model predictive control (MPC) based on Gaussian Process models (GPs) for the artificial pancreas (AP). The main novelty of the proposal is to exploit a GP that is trained on previously collected metabolic data of type 1 diabetes mellitus (T1DM) patients, to regulate the blood glucose levels by means of a personalized MPC strategy that automatically adjusts the basal insulin and the insulin boluses to be injected to the patients. The average closed-loop performance is improved in terms of classical indexes such as time in range, avoidance of critic hypoglycaemia episodes and avoidance of long-term hyperglycaemia events. The controller was evaluated in-silico by means of the FDA-accepted UVA/Padova metabolic simulator on 11 adult T1DM patients, showing promising results.
2023
Polver, Marco; Sonzogni, Beatrice; Mazzoleni, Mirko; Previdi, Fabio; Ferramosca, Antonio
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10446/260097
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