Zaitcev, A., Eissa, M.R., Hui, Z. et al. (3 more authors) (2020) A deep neural network application for improved prediction of HbA1c in type 1 diabetes. IEEE Journal of Biomedical and Health Informatics, 24 (10). pp. 2932-2941. ISSN 2168-2194
Abstract
HbA1c is a primary marker of long-term average blood glucose, which is an essential measure of successful control in type 1 diabetes. Previous studies have shown that HbA1c estimates can be obtained from 5- 12 weeks of daily blood glucose measurements. However, these methods suffer from accuracy limitations when applied to incomplete data with missing periods of measurements. The aim of this work is to overcome these limitations improving the accuracy and robustness of HbA1c prediction from time series of blood glucose. A novel data-driven HbA1c prediction model based on deep learning and convolutional neural networks is presented. The model focuses on the extraction of behavioral patterns from sequences of self-monitored blood glucose readings on various temporal scales. Assuming that subjects who share behavioral patterns have also similar capabilities for diabetes control and resulting HbA1c, it becomes possible to infer the HbA1c of subjects with incomplete data from multiple observations of similar behaviors. Trained and validated on a dataset, containing 1543 real world observation epochs from 759 subjects, the model has achieved the mean absolute error of 4.80±0.62 mmol/mol, median absolute error of 3.81±0.58 mmol/mol and R2 of 0.71±0.09 on average during the 10 fold cross validation. Automatic behavioral characterization via extraction of sequential features by the proposed convolutional neural network structure has significantly improved the accuracy of HbA1c prediction compared to the existing methods.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other users, 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 components of this work in other works. Reproduced in accordance with the publisher's self-archiving policy. |
Keywords: | Convolutional neural networks; diabetes; feature extraction; machine learning; regression analysis |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Electronic and Electrical Engineering (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 23 Jan 2020 10:39 |
Last Modified: | 22 Oct 2021 10:55 |
Status: | Published |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Identification Number: | 10.1109/jbhi.2020.2967546 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:155948 |