Conference paper ยท Journal article
Short Term Blood Glucose Prediction based on Continuous Glucose Monitoring Data
Brain Computer Interface, Digital Health, Department of Health Technology, Technical University of Denmark1
Department of Health Technology, Technical University of Denmark2
Cognitive Systems, Department of Applied Mathematics and Computer Science, Technical University of Denmark3
Department of Applied Mathematics and Computer Science, Technical University of Denmark4
Technical University of Denmark5
Novo Nordisk Foundation6
Continuous Glucose Monitoring (CGM) has enabled important opportunities for diabetes management. This study explores the use of CGM data as input for digital decision support tools. We investigate how Recurrent Neural Networks (RNNs) can be used for Short Term Blood Glucose (STBG) prediction and compare the RNNs to conventional time-series forecasting using Autoregressive Integrated Moving Average (ARIMA).
A prediction horizon up to 90 min into the future is considered. In this context, we evaluate both population-based and patient-specific RNNs and contrast them to patient-specific ARIMA models and a simple baseline predicting future observations as the last observed. We find that the population-based RNN model is the best performing model across the considered prediction horizons without the need of patient-specific data.
This demonstrates the potential of RNNs for STBG prediction in diabetes patients towards detecting/mitigating severe events in the STBG, in particular hypoglycemic events. However, further studies are needed in regards to the robustness and practical use of the investigated STBG prediction models.
Language: | English |
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Publisher: | IEEE |
Year: | 2020 |
Pages: | 5140-5145 |
Proceedings: | 42<sup>nd</sup> Annual International Conference of the IEEE Engineering in Medicine & Biology Society |
Series: | Proceedings of the Annual International Conference of the Ieee Engineering in Medicine and Biology Society, Embs |
Journal subtitle: | Enabling Innovative Technologies for Global Healthcare, Embc 2020 |
ISBN: | 1728119901 , 172811991X , 172811991x , 9781728119908 and 9781728119915 |
ISSN: | 15584615 , 1094687x , 1557170x and 26940604 |
Types: | Conference paper and Journal article |
DOI: | 10.1109/EMBC44109.2020.9176695 |
ORCIDs: | Mohebbi, Ali and Morup, Morten |