title: Neural network incorporating meal information improves accuracy of short-time prediction of glucose concentration.
abstract: Diabetes mellitus is one of the most common chronic diseases, and a clinically important task in its management is the prevention of hypo/hyperglycemic events. This can be achieved by exploiting continuous glucose monitoring (CGM) devices and suitable short-term prediction algorithms able to infer future glycemia in real time. In the literature, several methods for short-time glucose prediction have been proposed, most of which do not exploit information on meals, and use past CGM readings only. In this paper, we propose an algorithm for short-time glucose prediction using past CGM sensor readings and information on carbohydrate intake. The predictor combines a neural network (NN) model and a first-order polynomial extrapolation algorithm, used in parallel to describe, respectively, the nonlinear and the linear components of glucose dynamics. Information on the glucose rate of appearance after a meal is described by a previously published physiological model. The method is assessed on 20 simulated datasets and on 9 real Abbott FreeStyle Navigator datasets, and its performance is successfully compared with that of a recently proposed NN glucose predictor. Results suggest that exploiting meal information improves the accuracy of short-time glucose prediction.
authors.affiliation: Department of Information Engineering, University of Padova, 35137 Padova, Italy. zecchinc@dei.unipd.it
authors.forename: Chiara
authors.initials: C
authors.lastname: Zecchin
authors.forename: Andrea
authors.initials: A
authors.lastname: Facchinetti
authors.forename: Giovanni
authors.initials: G
authors.lastname: Sparacino
authors.forename: Giuseppe
authors.initials: G
authors.lastname: De Nicolao
authors.forename: Claudio
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D000465:Algorithms; D001786:Blood Glucose / Q000032:analysis*; D003198:Computer Simulation; D004040:Dietary Carbohydrates / Q000032:analysis*; D006801:Humans; D008954:Models, Biological; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D001786:Blood Glucose; D004040:Dietary Carbohydrates