Automatic International Classification of Diseases (ICD) coding plays a
crucial role in the extraction of relevant information from clinical notes for
proper recording and billing. One of the most important directions for boosting
the performance of automatic ICD coding is modeling ICD code relations.
However, current methods insufficiently model the intricate relationships among
ICD codes and often overlook the importance of context in clinical notes. In
this paper, we propose a novel approach, a contextualized and flexible
framework, to enhance the learning of ICD code representations. Our approach,
unlike existing methods, employs a dependent learning paradigm that considers
the context of clinical notes in modeling all possible code relations. We
evaluate our approach on six public ICD coding datasets and the experimental
results demonstrate the effectiveness of our approach compared to
state-of-the-art baselines.

自动国际疾病分类（ICD）编码在从临床记录中提取相关信息以进行正确记录和计费方面起着关键作用。在提高自动 ICD 编码性能方面，建模 ICD 代码关系是其中最重要的方向之一。然而，当前方法不足以充分建模 ICD 代码之间的复杂关系，并经常忽视临床记录中的上下文重要性。本文提出了一种新颖的方法，即一个上下文化和灵活的框架，以增强 ICD 代码表示的学习。与现有方法不同，我们的方法采用了一种依赖学习范式，考虑临床记录的上下文来建模所有可能的代码关系。我们在六个公共 ICD 编码数据集上评估了我们的方法，实验结果证明了与现有基线方法相比我们方法的有效性。