Interpreting Deep Glucose Predictive Models for Diabetic People Using RETAIN
This work addresses the need for interpretable deep learning models in biomedical applications, specifically for type-2 diabetic patients and practitioners, though it is incremental as it applies an existing method to a new domain.
The paper tackled the problem of forecasting future glucose values for diabetic people using the interpretable RETAIN architecture, showing that it outperforms a random forest model and equals an LSTM-based neural network on accuracy and clinical metrics.
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.