论文标题
通过注意力增强Heathcare中深层模型的解释性：应用糖尿病患者的葡萄糖预测
Enhancing the Interpretability of Deep Models in Heathcare Through Attention: Application to Glucose Forecasting for Diabetic People
论文作者
论文摘要
他们的“黑匣子”性质阻碍了医疗保健中深度学习的采用。在本文中，我们探讨了糖尿病患者胶状预测任务的保留架构。通过使用两级注意机制，可以解释基于重复的神经网络的保留模型。我们通过将其统计和临床性能与基于决策树的两个深层模型和三个模型进行比较，评估了2型IDIAB和1型OHIOT1DM数据集的保留模型。我们表明，保留模型在准确性和可解释性之间提供了非常好的折衷，几乎与LSTM和FCN模型一样准确，同时保持了可解释。我们通过分析每个变量对最终预测的贡献来展示其可解释性质的有用性。它表明，在葡萄糖预测之前，保留模型未使用超过一小时的信号值。另外，我们展示了在事件的到来（例如碳水化合物摄入量或胰岛素输注）的到来后，保留模型如何改变其行为。特别是，这表明事件发生前患者的状态对于预测特别重要。总体而言，由于其可解释性，保留模型似乎是医疗保健中回归或分类任务的非常合格的模型。
The adoption of deep learning in healthcare is hindered by their "black box" nature. In this paper, we explore the RETAIN architecture for the task of glusose forecasting for diabetic people. By using a two-level attention mechanism, the recurrent-neural-network-based RETAIN model is interpretable. We evaluate the RETAIN model on the type-2 IDIAB and the type-1 OhioT1DM datasets by comparing its statistical and clinical performances against two deep models and three models based on decision trees. We show that the RETAIN model offers a very good compromise between accuracy and interpretability, being almost as accurate as the LSTM and FCN models while remaining interpretable. We show the usefulness of its interpretable nature by analyzing the contribution of each variable to the final prediction. It revealed that signal values older than one hour are not used by the RETAIN model for the 30-minutes ahead of time prediction of glucose. Also, we show how the RETAIN model changes its behavior upon the arrival of an event such as carbohydrate intakes or insulin infusions. In particular, it showed that the patient's state before the event is particularily important for the prediction. Overall the RETAIN model, thanks to its interpretability, seems to be a very promissing model for regression or classification tasks in healthcare.
