HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding
This work solves the problem of efficient and accurate medical record classification for healthcare systems, representing an incremental advancement in automated ICD coding methods.
The paper tackles automated ICD coding by proposing HieNet, a bidirectional hierarchy framework that addresses challenges like heterogeneity and label imbalance, achieving state-of-the-art performance with significant improvements on two public datasets.
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.