Jump neural network for online short-time prediction of blood glucose from continuous monitoring sensors and meal information

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...

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Vydané v:	Computer methods and programs in biomedicine Ročník 113; číslo 1; s. 144 - 152
Hlavní autori:	Zecchin, C., Facchinetti, A., Sparacino, G., Cobelli, C.
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Kidlington Elsevier Ireland Ltd 01.01.2014 Elsevier
Predmet:	Algorithms Biological and medical sciences Blood Glucose - analysis Blood Glucose Self-Monitoring - instrumentation Continuous glucose monitoring Diabetes Diabetes Mellitus, Type 1 - blood Diabetes Mellitus, Type 1 - physiopathology Diabetes. Impaired glucose tolerance Endocrine pancreas. Apud cells (diseases) Endocrinopathies Exact sciences and technology Food Forecast Glucose Humans Inference from stochastic processes; time series analysis Internal Medicine Management. Various non-drug treatments. Langerhans islet grafts Mathematical models Mathematics Meals Medical sciences Monitoring Neural networks Neural Networks (Computer) Non-linear modeling Other Patients Probability and statistics Sciences and techniques of general use Sensors Signal processing Statistics Time series Continuous glucose monitoring Non-linear modeling Signal processing Diabetes Time series Forecast Short term Therapy Measurement sensor Meal Concentration Glucose Neural network Real time Modeling Timed system Blood Type 1 diabetes Carbohydrate Non linear effect Glycemia Monitoring
ISSN:	0169-2607, 1872-7565, 1872-7565
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Popis
Shrnutí:	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 30min) 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.
Bibliografia:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 23
ISSN:	0169-2607 1872-7565 1872-7565
DOI:	10.1016/j.cmpb.2013.09.016