Deep learning approaches exhibit promising performances on various text
tasks. However, they are still struggling on medical text classification since
samples are often extremely imbalanced and scarce. Different from existing
mainstream approaches that focus on supplementary semantics with external
medical information, this paper aims to rethink the data challenges in medical
texts and present a novel framework-agnostic algorithm called Text2Tree that
only utilizes internal label hierarchy in training deep learning models. We
embed the ICD code tree structure of labels into cascade attention modules for
learning hierarchy-aware label representations. Two new learning schemes,
Similarity Surrogate Learning (SSL) and Dissimilarity Mixup Learning (DML), are
devised to boost text classification by reusing and distinguishing samples of
other labels following the label representation hierarchy, respectively.
Experiments on authoritative public datasets and real-world medical records
show that our approach stably achieves superior performances over classical and
advanced imbalanced classification methods.

本文提出了一种名为 Text2Tree 的新型算法，该算法只利用内部标签层次结构来训练深度学习模型，通过嵌入 ICD 标签树结构到级联注意力模块中，学习具有层次感知的标签表示。引入了相似度替代学习（SSL）和差异混合学习（DML）两种新的学习方案，通过重用和区分其他标签的样本来提高文本分类性能。实验证明，我们的方法在公共数据集和真实医疗记录上表现出优于传统和先进的不平衡分类方法的稳定性能。