论文标题
代码同义词确实很重要：自动ICD编码的多个同义词匹配网络
Code Synonyms Do Matter: Multiple Synonyms Matching Network for Automatic ICD Coding
论文作者
论文摘要
自动ICD编码定义为将疾病代码分配给电子病历（EMRS）。现有方法通常将注意力与代码表示相关，以匹配相关的文本片段。与这些用代码层次结构或描述对标签进行建模的作品不同，我们认为代码同义词可以基于观察到EMR中的代码表达式与ICD中的描述不同的观察结果提供更全面的知识。通过将代码与UML中的概念保持一致，我们收集每个代码的同义词。然后，我们提出了一个匹配网络的多个同义词，以利用同义词来提供更好的代码表示学习，并最终帮助代码分类。模拟III数据集的实验表明，我们所提出的方法的表现优于先前的最新方法。
Automatic ICD coding is defined as assigning disease codes to electronic medical records (EMRs). Existing methods usually apply label attention with code representations to match related text snippets. Unlike these works that model the label with the code hierarchy or description, we argue that the code synonyms can provide more comprehensive knowledge based on the observation that the code expressions in EMRs vary from their descriptions in ICD. By aligning codes to concepts in UMLS, we collect synonyms of every code. Then, we propose a multiple synonyms matching network to leverage synonyms for better code representation learning, and finally help the code classification. Experiments on the MIMIC-III dataset show that our proposed method outperforms previous state-of-the-art methods.
