title: Model individualization for artificial pancreas.
abstract: OBJECTIVE
  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.
  
  
  METHODS
  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.
  
  
  RESULTS
  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.
  
  
  CONCLUSIONS
  The approaches proposed in this work have shown a good potential to identify glucose-insulin models for designing individualized control laws for artificial pancreas.
authors.affiliation: Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy. Electronic address: mirko.messori01@ateneopv.it.
authors.forename: Mirko
authors.initials: M
authors.lastname: Messori
authors.affiliation: Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy.
authors.forename: Chiara
authors.initials: C
authors.lastname: Toffanin
authors.affiliation: Department of Information Engineering, University of Padova, Padova, Italy.
authors.forename: Simone
authors.initials: S
authors.lastname: Del Favero
authors.affiliation: Department of Industrial and Information Engineering, University of Pavia, Pavia, Italy.
authors.forename: Giuseppe
authors.initials: G
authors.lastname: De Nicolao
authors.affiliation: Department of Information Engineering, University of Padova, Padova, Italy.
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
authors.affiliation: Department of Civil Engineering and Architecture, University of Pavia, Pavia, Italy.
authors.forename: Lalo
authors.initials: L
authors.lastname: Magni
mesh_terms: D000465:Algorithms; D003922:Diabetes Mellitus, Type 1 / Q000188:drug therapy; D006801:Humans; D007328:Insulin / Q000008:administration & dosage*; D019397:Pancreas, Artificial / Q000592:standards*
chemical_list: D007328:Insulin
keywords: Constrained optimization; Linear systems; Model predictive control; Nonparametric identification; Type 1 diabetes