Clinical-Coder: Assigning Interpretable ICD-10 Codes to Chinese Clinical Notes

Pengfei Cao, Chenwei Yan, Xiangling Fu, Yubo Chen, Kang Liu, Jun Zhao, Shengping Liu, Weifeng Chong


Abstract
In this paper, we introduce Clinical-Coder, an online system aiming to assign ICD codes to Chinese clinical notes. ICD coding has been a research hotspot of clinical medicine, but the interpretability of prediction hinders its practical application. We exploit a Dilated Convolutional Attention network with N-gram Matching mechanism (DCANM) to capture semantic features for non-continuous words and continuous n-gram words, concentrating on explaining the reason why each ICD code to be predicted. The experiments demonstrate that our approach is effective and that our system is able to provide supporting information in clinical decision making.
Anthology ID:
2020.acl-demos.33
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics: System Demonstrations
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
294–301
URL:
https://www.aclweb.org/anthology/2020.acl-demos.33
DOI:
Bib Export formats:
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PDF:
https://www.aclweb.org/anthology/2020.acl-demos.33.pdf
Software:
 2020.acl-demos.33.Software.zip

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