title: Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information.
abstract: Several real-time short-term prediction methods, based on time-series modeling of past continuous glucose monitoring (CGM) sensor data have been proposed with the aim of allowing the patient, on the basis of predicted glucose concentration, to anticipate therapeutic decisions and improve therapy of type 1 diabetes. In this field, neural network (NN) approaches could improve prediction performance handling in their inputs additional information. In this contribution we propose a jump NN prediction algorithm (horizon 30 min) that exploits not only past CGM data but also ingested carbohydrates information. The NN is tuned on data of 10 type 1 diabetics and then assessed on 10 different subjects. Results show that predictions of glucose concentration are accurate and comparable to those obtained by a recently proposed NN approach (Zecchin et al. (2012) [26]) having higher structural and algorithmical complexity and requiring the patient to announce the meals. This strengthen the potential practical usefulness of the new jump NN approach.
authors.affiliation: Department of Information Engineering, University of Padova, 35131 Padova, Italy.
authors.forename: C
authors.initials: C
authors.lastname: Zecchin
authors.forename: A
authors.initials: A
authors.lastname: Facchinetti
authors.forename: G
authors.initials: G
authors.lastname: Sparacino
authors.forename: C
authors.initials: C
authors.lastname: Cobelli
mesh_terms: D001786:Blood Glucose / Q000032:analysis*; D015190:Blood Glucose Self-Monitoring / Q000295:instrumentation*; D003922:Diabetes Mellitus, Type 1 / Q000097:blood* / Q000503:physiopathology; D005502:Food; D006801:Humans; D016571:Neural Networks, Computer
chemical_list: D001786:Blood Glucose
keywords: Continuous glucose monitoring; Diabetes; Forecast; Non-linear modeling; Signal processing; Time series