  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 datasets from 20 real patients. The proposed
network outperforms the baseline methods in terms of all evaluation criteria.
