论文标题
使用保留来解释糖尿病患者的深葡萄糖预测模型
Interpreting Deep Glucose Predictive Models for Diabetic People Using RETAIN
论文作者
论文摘要
通过使用深度学习，生物医学领域的进展受到模型缺乏解释性的阻碍。在本文中，我们研究了对糖尿病患者未来葡萄糖值的预测的保留结构。由于其两级注意机制，保留模型是可以解释的，同时保持与标准神经网络一样高效。我们在现实世界中的2型糖尿病人群上评估了该模型，并将其与随机森林模型和基于LSTM的复发性神经网络进行了比较。我们的结果表明，保留模型的表现优于前者，并根据共同的准确度指标和临床可接受性指标等于后者，从而证明了其在葡萄糖水平预测的背景下的合法性。此外，我们提出了利用可解释的性质的工具。对于患者而言，它与从业者一样，它可以增强对模型预测的理解，并改善未来葡萄糖预测模型的设计。
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.
