A Two-Stage Decoder for Efficient ICD Coding

Thanh-Tung Nguyen, Viktor Schlegel, Abhinav Ramesh Kashyap, Stefan Winkler

Abstract
Clinical notes in healthcare facilities are tagged with the International Classification of Diseases (ICD) code; a list of classification codes for medical diagnoses and procedures. ICD coding is a challenging multilabel text classification problem due to noisy clinical document inputs and long-tailed label distribution. Recent automated ICD coding efforts improve performance by encoding medical notes and codes with additional data and knowledge bases. However, most of them do not reflect how human coders generate the code: first, the coders select general code categories and then look for specific subcategories that are relevant to a patient’s condition. Inspired by this, we propose a two-stage decoding mechanism to predict ICD codes. Our model uses the hierarchical properties of the codes to split the prediction into two steps: At first, we predict the parent code and then predict the child code based on the previous prediction. Experiments on the public MIMIC-III data set have shown that our model performs well in single-model settings without external data or knowledge.
Anthology ID:
2023.findings-acl.285
Volume:
Findings of the Association for Computational Linguistics: ACL 2023
Month:
July
Year:
2023
Address:
Toronto, Canada
Editors:
Anna Rogers, Jordan Boyd-Graber, Naoaki Okazaki
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4658–4665
Language:
URL:
https://aclanthology.org/2023.findings-acl.285
DOI:
10.18653/v1/2023.findings-acl.285
Bibkey:
Cite (ACL):
Thanh-Tung Nguyen, Viktor Schlegel, Abhinav Ramesh Kashyap, and Stefan Winkler. 2023. A Two-Stage Decoder for Efficient ICD Coding. In Findings of the Association for Computational Linguistics: ACL 2023, pages 4658–4665, Toronto, Canada. Association for Computational Linguistics.
Cite (Informal):
A Two-Stage Decoder for Efficient ICD Coding (Nguyen et al., Findings 2023)
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https://preview.aclanthology.org/ingest-acl-2023-videos/2023.findings-acl.285.pdf
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