Kezhi Li
Convolutional Recurrent Neural Networks for Glucose Prediction
Li, Kezhi; Daniels, John; Liu, Chengyuan; Herrero-Vinas, Pau; Georgiou, Pantelis
Authors
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. |
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