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    <abstract>The International Classification of Diseases (ICD) provides a standardized way for classifying diseases, which endows each disease with a unique code. ICD coding aims to assign proper ICD codes to a medical record. Since manual coding is very laborious and prone to errors, many methods have been proposed for the automatic ICD coding task. However, most of existing methods independently predict each code, ignoring two important characteristics: Code Hierarchy and Code Co-occurrence. In this paper, we propose a Hyperbolic and Co-graph Representation method (HyperCore) to address the above problem. Specifically, we propose a hyperbolic representation method to leverage the code hierarchy. Moreover, we propose a graph convolutional network to utilize the code co-occurrence. Experimental results on two widely used datasets demonstrate that our proposed model outperforms previous state-of-the-art methods.</abstract>
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%0 Conference Proceedings
%T HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding
%A Cao, Pengfei
%A Chen, Yubo
%A Liu, Kang
%A Zhao, Jun
%A Liu, Shengping
%A Chong, Weifeng
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F cao-etal-2020-hypercore
%X The International Classification of Diseases (ICD) provides a standardized way for classifying diseases, which endows each disease with a unique code. ICD coding aims to assign proper ICD codes to a medical record. Since manual coding is very laborious and prone to errors, many methods have been proposed for the automatic ICD coding task. However, most of existing methods independently predict each code, ignoring two important characteristics: Code Hierarchy and Code Co-occurrence. In this paper, we propose a Hyperbolic and Co-graph Representation method (HyperCore) to address the above problem. Specifically, we propose a hyperbolic representation method to leverage the code hierarchy. Moreover, we propose a graph convolutional network to utilize the code co-occurrence. Experimental results on two widely used datasets demonstrate that our proposed model outperforms previous state-of-the-art methods.
%R 10.18653/v1/2020.acl-main.282
%U https://aclanthology.org/2020.acl-main.282
%U https://doi.org/10.18653/v1/2020.acl-main.282
%P 3105-3114

Markdown (Informal)

[HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding](https://aclanthology.org/2020.acl-main.282) (Cao et al., ACL 2020)

• HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding (Cao et al., ACL 2020)

ACL

• Pengfei Cao, Yubo Chen, Kang Liu, Jun Zhao, Shengping Liu, and Weifeng Chong. 2020. HyperCore: Hyperbolic and Co-graph Representation for Automatic ICD Coding. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3105–3114, Online. Association for Computational Linguistics.