Model individualization for artificial pancreas

• Published in: Computer methods and programs in biomedicine Vol. 171; pp. 133 - 140
• Main Authors: Messori, Mirko, Toffanin, Chiara, Del Favero, Simone, De Nicolao, Giuseppe, Cobelli, Claudio, Magni, Lalo
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.04.2019
• Subjects: Constrained optimization; Internal Medicine; Linear systems; Model predictive control; Nonparametric identification; Other; Type 1 diabetes; Linear systems; Model predictive control; Type 1 diabetes; Nonparametric identification; Constrained optimization; model predictive control; nonparametric identification; type 1 diabetes; linear systems
• DOI: 10.1016/j.cmpb.2016.06.006
• ISSN: 0169-2607

•Two approaches are proposed for identifying tailored linear models describing the dynamics of patients with type 1 diabetes.•The first method is a black box identification based on a novel kernel-based nonparametric approach.•The second is a grey box identification that relies on constrained optimization and requires a pre-defined model structure.•The individualized models are evaluated in simulation on the adult virtual population of the UVA/Padova simulator.•The resulting simulation performance is significantly improved with respect to a linear average model.•The proposed approaches can identify glucose-insulin models for designing individualized control laws for articial pancreas. The inter-subject variability characterizing the patients affected by type 1 diabetes mellitus makes automatic blood glucose control very challenging. Different patients have different insulin responses, and a control law based on a non-individualized model could be ineffective. The definition of an individualized control law in the context of artificial pancreas is currently an open research topic. In this work we consider two novel identification approaches that can be used for individualizing linear glucose–insulin models to a specific patient. The first approach belongs to the class of black-box identification and is based on a novel kernel-based nonparametric approach, whereas the second is a gray-box identification technique which relies on a constrained optimization and requires to postulate a model structure as prior knowledge. The latter is derived from the linearization of the average nonlinear adult virtual patient of the UVA/Padova simulator. Model identification and validation are based on in silico data collected during simulations of clinical protocols designed to produce a sufficient signal excitation without compromising patient safety. The identified models are evaluated in terms of prediction performance by means of the coefficient of determination, fit, positive and negative max errors, and root mean square error. Both identification approaches were used to identify a linear individualized glucose–insulin model for each adult virtual patient of the UVA/Padova simulator. The resulting model simulation performance is significantly improved with respect to the performance achieved by a linear average model. The approaches proposed in this work have shown a good potential to identify glucose–insulin models for designing individualized control laws for artificial pancreas.