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.
antonio.ferramosca@unibg.it
12-giu-2022
2023
Inglese
22nd IFAC World Congress. Yokohama, Japan, July 9-14, 2023 Proceedings
Ishii, Hideaki; Ebihara, Yoshio; Imura, Jun-ichi; Yamakita, Masaki;
56
2
9642
9647
online
United Kingdom
Kidlington
Elsevier
22nd IFAC World Congress, Yokohama, Japan, 9-14 July 2023
22nd
Yokohama (Japan)
9-14 July 2023
internazionale
su invito
Settore ING-INF/04 - Automatica
Artificial Pancreas; Model Predictive Control; Data-driven Control; Gaussian Processes;
   ANTHEM - AdvaNced Technologies for Human-centrEd Medicine
   ANTHEM
   MUR - MINISTERO DELL'UNIVERSITA' E DELLA RICERCA
info:eu-repo/semantics/conferenceObject
5
Polver, Marco; Sonzogni, Beatrice; Mazzoleni, Mirko; Previdi, Fabio; Ferramosca, Antonio
1.4 Contributi in atti di convegno - Contributions in conference proceedings::1.4.01 Contributi in atti di convegno - Conference presentations
open
Non definito
273
(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
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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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