Computer Science > Artificial Intelligence
arXiv:2212.04891 (cs)
[Submitted on 9 Dec 2022]
Title:HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding
View a PDF of the paper titled HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding, by Shi Wang and Daniel Tang and Luchen Zhang and Huilin Li and Ding Han
View PDFAbstract: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.
| Subjects: | Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2212.04891 [cs.AI] |
| (orarXiv:2212.04891v1 [cs.AI] for this version) | |
| https://doi.org/10.48550/arXiv.2212.04891 arXiv-issued DOI via DataCite |
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View a PDF of the paper titled HieNet: Bidirectional Hierarchy Framework for Automated ICD Coding, by Shi Wang and Daniel Tang and Luchen Zhang and Huilin Li and Ding Han
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