# Interpreting Deep Glucose Predictive Models for Diabetic People Using RETAIN

arXiv: https://arxiv.org/abs/2009.04524 · HTML: https://mlsanity.com/paper/2009.04524/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 8 Sep 2020.

## Built on by (most cited first, 2 of 2)

- [Explainable AI for clinical and remote health applications: a survey on tabular and time series data](https://mlsanity.com/paper/2209.06528/) (2022)
- [A Review on Machine Learning Approaches for the Prediction of Glucose Levels and Hypogylcemia](https://mlsanity.com/paper/2601.11615/) (2026)

## Abstract (arXiv)

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.