Skip to main content

Research Repository

Advanced Search

Convolutional Recurrent Neural Networks for Glucose Prediction

Li, Kezhi; Daniels, John; Liu, Chengyuan; Herrero-Vinas, Pau; Georgiou, Pantelis

Convolutional Recurrent Neural Networks for Glucose Prediction Thumbnail


Authors

Kezhi Li

John Daniels

Chengyuan Liu

Pau Herrero-Vinas

Pantelis Georgiou



Abstract

© 2013 IEEE. Control of blood glucose is essential for diabetes management. Current digital therapeutic approaches for subjects with type 1 diabetes mellitus such as the artificial pancreas and insulin bolus calculators leverage machine learning techniques for predicting subcutaneous glucose for improved control. Deep learning has recently been applied in healthcare and medical research to achieve state-of-the-art results in a range of tasks including disease diagnosis, and patient state prediction among others. In this paper, we present a deep learning model that is capable of forecasting glucose levels with leading accuracy for simulated patient cases (root-mean-square error (RMSE) = 9.38 ± 0.71 [mg/dL] over a 30-min horizon, RMSE = 18.87 ± 2.25 [mg/dL] over a 60-min horizon) and real patient cases (RMSE = 21.07 ± 2.35 [mg/dL] for 30 min, RMSE = 33.27 ± 4.79% for 60 min). In addition, the model provides competitive performance in providing effective prediction horizon (PHeff) with minimal time lag both in a simulated patient dataset (PHeff = 29.0 ± 0.7 for 30 min and PHeff = 49.8 ± 2.9 for 60 min) and in a real patient dataset (PHeff = 19.3 ± 3.1 for 30 min and PHeff = 29.3 ± 9.4 for 60 min). This approach is evaluated on a dataset of ten simulated cases generated from the UVA/Padova simulator and a clinical dataset of ten real cases each containing glucose readings, insulin bolus, and meal (carbohydrate) data. Performance of the recurrent convolutional neural network is benchmarked against four algorithms. The proposed algorithm is implemented on an Android mobile phone, with an execution time of 6 ms on a phone compared to an execution time of 780 ms on a laptop.

Citation

Li, K., Daniels, J., Liu, C., Herrero-Vinas, P., & Georgiou, P. (2020). Convolutional Recurrent Neural Networks for Glucose Prediction. IEEE Journal of Biomedical and Health Informatics, 24(2), 603-613. https://doi.org/10.1109/jbhi.2019.2908488

Journal Article Type Article
Acceptance Date Mar 25, 2019
Online Publication Date Apr 1, 2019
Publication Date 2020-02
Deposit Date Jul 12, 2019
Publicly Available Date Mar 29, 2024
Journal IEEE Journal of Biomedical and Health Informatics
Electronic ISSN 2168-2208
Publisher Institute of Electrical and Electronics Engineers
Peer Reviewed Peer Reviewed
Volume 24
Issue 2
Pages 603-613
DOI https://doi.org/10.1109/jbhi.2019.2908488
Keywords Biotechnology; Electrical and Electronic Engineering; Health Information Management; Computer Science Applications
Public URL https://nottingham-repository.worktribe.com/output/2302666
Publisher URL https://ieeexplore.ieee.org/document/8678399
Additional Information © 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Files




You might also like



Downloadable Citations