The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network
Open accessAbstract
A deep learning network was used to predict future blood glucose levels, as this can permit diabetes patients to take action before imminent hyperglycaemia and hypoglycaemia. A sequential model with one long-short-term memory (LSTM) layer, one bidirectional LSTM layer and several fully connected layers was used to predict blood glucose levels for different prediction horizons. The method was trained and tested on 26 retrospectively analysed datasets from 20 real patients. The proposed network outperforms the baseline methods in terms of all evaluation criteria.
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Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Bidirectional recurrent neural networks
Schuster, M., & Paliwal, K. K. (1997) · IEEE Transactions on Signal Processing · doi:10.1109/78.650093 · Cited by 10,172
Sensor-Augmented Pump Therapy for A1C Reduction (STAR 3) Study
Bergenstal, R. M., Tamborlane, W. V., Ahmann, A., Buse, J. B., Dailey, G., Davis, S. N., Joyce, C., Perkins, B. A., Welsh, J. B., Willi, S. M., et al. (2011) · Diabetes Care · doi:10.2337/dc11-1248 · Cited by 118
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Prediction for the spread of COVID-19 in India and effectiveness of preventive measures
Tomar, A., & Gupta, N. (2020) · Science of The Total Environment · doi:10.1016/j.scitotenv.2020.138762 · Cited by 445
An intelligent healthcare monitoring framework using wearable sensors and social networking data
Ali, F., El-Sappagh, S., Islam, S. M. R., Ali, A., Attique, M., Imran, M., & Kwak, K.-S. (2021) · Future Generation Computer Systems · doi:10.1016/j.future.2020.07.047 · Cited by 355
Multi-step ahead predictive model for blood glucose concentrations of type-1 diabetic patients
Zaidi, S. M. A., Chandola, V., Ibrahim, M., Romanski, B., Mastrandrea, L. D., & Singh, T. (2021) · Scientific Reports · doi:10.1038/s41598-021-03341-5 · Cited by 19
Metadata
- Authors
- Sun, Q., Jankovic, M. V., Bally, L., & Mougiakakou, S. G.
- Venue
- 2018 14th Symposium on Neural Networks and Applications (NEUREL)
- Publisher
- IEEE
- Published
- 2018-11
- Pages
- 1-5
- OpenAlex
- W2963922828
- Cited by
- 180
- References
- 3
- FWCI
- 88.72
- Type
- conference paper
- Language
- en
- Open access
- green
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- Sun, Q., Jankovic, M. V., Bally, L., & Mougiakakou, S. G. (2018). Predicting Blood Glucose with an LSTM and Bi-LSTM Based Deep Neural Network. 2018 14th Symposium on Neural Networks and Applications (NEUREL). https://doi.org/10.1109/neurel.2018.8586990
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
