Nemat, H., Khadem, H. orcid.org/0000-0002-6878-875X, Elliott, J. et al. (1 more author) (2020) Data fusion of activity and CGM for predicting blood glucose levels. In: Bach, K., Bunescu, R., Marling, C. and Wiratunga, N., (eds.) Knowledge Discovery in Healthcare Data 2020. 5th International Workshop on Knowledge Discovery in Healthcare Data co-located with 24th European Conference on Artificial Intelligence (ECAI 2020), 29-30 Aug 2020, Santiago de Compostela, Spain (virtual). CEUR Workshop Proceedings , pp. 120-124.
Abstract
This work suggests two methods—both relying on stacked regression and data fusion of CGM and activity—to predict the blood glucose level of patients with type 1 diabetes. Method 1 uses histories of CGM data appended with the average of activity data in the same histories to train three base regressions: a multilayer perceptron, a long short- term memory, and a partial least squares regression. In Method 2, histories of CGM and activity data are used separately to train the same base regressions. In both methods, the predictions from the base regressions are used as features to create a combined model. This model is then used to make the final predictions. The results obtained show the effectiveness of both methods. Method 1 provides slightly better results.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Editors: |
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Copyright, Publisher and Additional Information: | © 2020 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (http://creativecommons.org/licenses/by/4.0). |
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: | 20 Oct 2020 09:15 |
Last Modified: | 20 Oct 2020 19:00 |
Published Version: | http://ceur-ws.org/Vol-2675 |
Status: | Published |
Publisher: | CEUR Workshop Proceedings |
Refereed: | Yes |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:166816 |