Abstract
ICD(International Classification of Diseases) coding involves assigning ICD codes to patients visit based on their medical notes. ICD coding is a challenging multilabel text classification problem due to noisy medical document inputs. Recent advancements in automated ICD coding have enhanced performance by integrating additional data and knowledge bases with the encoding of medical notes and codes. However, most of them ignore the code hierarchy, leading to improper code assignments. To address these problems, we propose a novel framework based on associated and hierarchical code description distillation (AHDD) for better code representation learning and avoidance of improper code assignment.we utilize the code description and the hierarchical structure inherent to the ICD codes. Therefore, in this paper, we leverage the code description and the hierarchical structure inherent to the ICD codes. The code description is also applied to aware the attention layer and output layer. Experimental results on the benchmark dataset show the superiority of the proposed framework over several state-of-the-art baselines.
Abstract (translated)
ICD（国际疾病分类）编码是将患者访问分配给他们的医疗记录的ICD代码的过程。由于嘈杂的医疗文件输入，ICD编码是一个具有多个标签的多标签文本分类问题。最近，自动ICD编码通过将额外数据和知识库与医疗记录的编码相结合来提高性能。然而，大多数忽略代码层次结构，导致不当的代码分配。为了应对这些问题，我们提出了一个基于相关和分层代码描述蒸馏（AHDD）的新框架，以进行更好的代码表示学习和避免不当代码分配。我们利用了ICD代码固有的代码描述和层次结构。因此，在本文中，我们利用了ICD代码的代码描述和层次结构。将代码描述还应用于注意层和输出层，以增强模型的关注度。基准数据集上的实验结果表明，与最先进的基线相比，所提出的框架具有优越性。
URL
https://arxiv.org/abs/2404.11132
