@inproceedings{zhou-etal-2021-automatic,
title = "Automatic {ICD} Coding via Interactive Shared Representation Networks with Self-distillation Mechanism",
author = "Zhou, Tong and
Cao, Pengfei and
Chen, Yubo and
Liu, Kang and
Zhao, Jun and
Niu, Kun and
Chong, Weifeng and
Liu, Shengping",
editor = "Zong, Chengqing and
Xia, Fei and
Li, Wenjie and
Navigli, Roberto",
booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
month = aug,
year = "2021",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.acl-long.463",
doi = "10.18653/v1/2021.acl-long.463",
pages = "5948--5957",
abstract = "The ICD coding task aims at assigning codes of the International Classification of Diseases in clinical notes. Since manual coding is very laborious and prone to errors, many methods have been proposed for the automatic ICD coding task. However, existing works either ignore the long-tail of code frequency or the noisy clinical notes. To address the above issues, we propose an Interactive Shared Representation Network with Self-Distillation Mechanism. Specifically, an interactive shared representation network targets building connections among codes while modeling the co-occurrence, consequently alleviating the long-tail problem. Moreover, to cope with the noisy text issue, we encourage the model to focus on the clinical note{'}s noteworthy part and extract valuable information through a self-distillation learning mechanism. Experimental results on two MIMIC datasets demonstrate the effectiveness of our method.",
}
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%0 Conference Proceedings
%T Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism
%A Zhou, Tong
%A Cao, Pengfei
%A Chen, Yubo
%A Liu, Kang
%A Zhao, Jun
%A Niu, Kun
%A Chong, Weifeng
%A Liu, Shengping
%Y Zong, Chengqing
%Y Xia, Fei
%Y Li, Wenjie
%Y Navigli, Roberto
%S Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
%D 2021
%8 August
%I Association for Computational Linguistics
%C Online
%F zhou-etal-2021-automatic
%X The ICD coding task aims at assigning codes of the International Classification of Diseases in clinical notes. Since manual coding is very laborious and prone to errors, many methods have been proposed for the automatic ICD coding task. However, existing works either ignore the long-tail of code frequency or the noisy clinical notes. To address the above issues, we propose an Interactive Shared Representation Network with Self-Distillation Mechanism. Specifically, an interactive shared representation network targets building connections among codes while modeling the co-occurrence, consequently alleviating the long-tail problem. Moreover, to cope with the noisy text issue, we encourage the model to focus on the clinical note’s noteworthy part and extract valuable information through a self-distillation learning mechanism. Experimental results on two MIMIC datasets demonstrate the effectiveness of our method.
%R 10.18653/v1/2021.acl-long.463
%U https://aclanthology.org/2021.acl-long.463
%U https://doi.org/10.18653/v1/2021.acl-long.463
%P 5948-5957
Markdown (Informal)
[Automatic ICD Coding via Interactive Shared Representation Networks with Self-distillation Mechanism](https://aclanthology.org/2021.acl-long.463) (Zhou et al., ACL-IJCNLP 2021)
ACL