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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Veröffentlicht in:	Computer methods and programs in biomedicine Jg. 113; H. 1; S. 144 - 152
Hauptverfasser:	Zecchin, C., Facchinetti, A., Sparacino, G., Cobelli, C.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Kidlington Elsevier Ireland Ltd 01.01.2014 Elsevier
Schlagworte:	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
Online-Zugang:	Volltext
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