Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/NEUREL.2018.8586990 | 2018 14th Symposium on Neural Networks and Applications (NEUREL) |
Keywords | Field | DocType |
blood glucose level,diabetes,prediction,long-short-term memory network | Artificial intelligence,Deep learning,Sequential model,Artificial neural network,Mathematics,Machine learning | Journal |
Volume | ISBN | Citations |
abs/1809.03817 | 978-1-5386-6975-4 | 2 |
PageRank | References | Authors |
0.38 | 8 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Qingnan Sun | 1 | 2 | 1.05 |
| Marko V. Jankovic | 2 | 2 | 0.72 |
| Lia Bally | 3 | 2 | 0.38 |
| Stavroula G Mougiakakou | 4 | 342 | 28.61 |