  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.
