In this work, we propose three explainable deep learning architectures to
automatically detect patients with Alzheimer`s disease based on their language
abilities. The architectures use: (1) only the part-of-speech features; (2)
only language embedding features and (3) both of these feature classes via a
unified architecture. We use self-attention mechanisms and interpretable
1-dimensional ConvolutionalNeural Network (CNN) to generate two types of
explanations of the model`s action: intra-class explanation and inter-class
explanation. The inter-class explanation captures the relative importance of
each of the different features in that class, while the inter-class explanation
captures the relative importance between the classes. Note that although we
have considered two classes of features in this paper, the architecture is
easily expandable to more classes because of its modularity. Extensive
experimentation and comparison with several recent models show that our method
outperforms these methods with an accuracy of 92.2% and F1 score of 0.952on the
DementiaBank dataset while being able to generate explanations. We show by
examples, how to generate these explanations using attention values.

本文提出了三种可解释的深度学习体系结构，基于患者的语言能力自动检测阿尔茨海默病患者。这些体系结构使用词性特征、语言嵌入特征或这两种特征的统一体系结构，并使用自注意机制和可解释的一维卷积神经网络生成模型操作的两种类型解释：类内解释和类间解释。通过大量实验证明，该方法在 DementiaBank 数据集上的准确率为 92.2％，F1 分数为 0.952，同时能够生成解释。