HieNet：用于自动化ICD编码的双向分层框架
HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding
摘要：
国际疾病分类（ICD）是一套用于医疗记录的分类代码。自动化ICD编码为每一份病历分配唯一的国际疾病分类编码，因其效率高和避免错误的优点而被广泛使用。然而，仍然存在诸如异质性、标签不平衡以及ICD代码之间的复杂关系等挑战。在这项工作中，我们提出了一种新的双向层次结构框架（HieNet），以应对挑战。具体地说，个性化的PageRank例程被开发用于捕获代码的相关性，双向层次通道编码器被开发用于捕获代码的层次表示，然后提出渐进预测方法以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了我们的方法。在两个权威公共数据集上的实验结果表明，我们提出的方法大大提高了最先进的性能。
英文摘要：
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.
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