  Progress in the biomedical field through the use of deep learning is hindered
by the lack of interpretability of the models. In this paper, we study the
RETAIN architecture for the forecasting of future glucose values for diabetic
people. Thanks to its two-level attention mechanism, the RETAIN model is
interpretable while remaining as efficient as standard neural networks. We
evaluate the model on a real-world type-2 diabetic population and we compare it
to a random forest model and a LSTM-based recurrent neural network. Our results
show that the RETAIN model outperforms the former and equals the latter on
common accuracy metrics and clinical acceptability metrics, thereby proving its
legitimacy in the context of glucose level forecasting. Furthermore, we propose
tools to take advantage of the RETAIN interpretable nature. As informative for
the patients as for the practitioners, it can enhance the understanding of the
predictions made by the model and improve the design of future glucose
predictive models.
