论文标题
Hienet：自动化ICD编码的双向层次结构框架
HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding
论文作者
论文摘要
国际疾病分类（ICD）是一组医疗记录的分类代码。自动化ICD编码通过每种病历分配了独特的疾病代码国际分类，最近广泛用于其效率和避免出现错误的效率。但是，仍然存在一些挑战，例如异质性，标签不平衡以及ICD代码之间的复杂关系。在这项工作中，我们提出了一个新颖的双向层次结构框架（HIENET）来应对挑战。具体而言，开发了个性化的Pagerank例程，以捕获代码的共同关系，双向层次结构段段编码器以捕获代码的层次结构表示，然后提出了渐进的预测方法来缩小预测的语义搜索空间。我们在两个广泛使用的数据集上验证我们的方法。两个权威公共数据集的实验结果表明，我们提出的方法可以通过很大的利润来提高最先进的绩效。
International Classification of Diseases (ICD) is a set of classification codes for medical records. Automated ICD coding, which assigns unique International Classification of Diseases codes with each medical record, is widely used recently for its efficiency and error-prone avoidance. However, there are challenges that remain such as heterogeneity, label unbalance, and complex relationships between ICD codes. In this work, we proposed a novel Bidirectional Hierarchy Framework(HieNet) to address the challenges. Specifically, a personalized PageRank routine is developed to capture the co-relation of codes, a bidirectional hierarchy passage encoder to capture the codes' hierarchical representations, and a progressive predicting method is then proposed to narrow down the semantic searching space of prediction. We validate our method on two widely used datasets. Experimental results on two authoritative public datasets demonstrate that our proposed method boosts state-of-the-art performance by a large margin.
