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Published in: Neural Computing and Applications 9/2021

03-08-2020 | Original Article

Long short-term memory neural network for glucose prediction

Authors: Jaime Carrillo-Moreno, Carmen Pérez-Gandía, Rafael Sendra-Arranz, Gema García-Sáez, M. Elena Hernando, Álvaro Gutiérrez

Published in: Neural Computing and Applications | Issue 9/2021

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Abstract

Diabetes is a chronic disease that affects a high percentage of the world population and produces different and serious complications to patients. Most diabetes complications may be avoided by controlling the blood glucose levels exhaustively. Moreover, a prediction of future glucose levels has shown to be fundamental in helping patients to plan and modify their treatment in real-time. In this paper, a glucose predictor based on long short-term memory neural networks is designed. Three input parameters are fed to the predictor: past glucose levels obtained from a continuous glucose monitoring sensor, the insulin units administered by an insulin pump and the patient’s carbohydrates intake. Different prediction times and input dimensions have been evaluated in order to provide the best prediction to patients. Results encourage the use of glucose predictions to avoid the occurrence of hypoglycemias, anticipate correction actions, and to increase the quality of life of these patients.

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Metadata
Title
Long short-term memory neural network for glucose prediction
Authors
Jaime Carrillo-Moreno
Carmen Pérez-Gandía
Rafael Sendra-Arranz
Gema García-Sáez
M. Elena Hernando
Álvaro Gutiérrez
Publication date
03-08-2020
Publisher
Springer London
Published in
Neural Computing and Applications / Issue 9/2021
Print ISSN: 0941-0643
Electronic ISSN: 1433-3058
DOI
https://doi.org/10.1007/s00521-020-05248-0

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