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    <abstract>Automatic medical coding assigns ICD codes to clinical notes, but it remains challenging due to long documents, imbalanced label distributions, and diverse terms. These challenges are especially severe for rare codes, which have limited training instances and are easily confused with semantically similar labels. We introduce **CoLa-ICD**, a knowledge-enhanced framework for long-tail prediction. CoLa-ICD enriches ICD labels with external terms, models dependencies among related codes, and learns stronger alignment between label semantics and clinical evidence for long-tail prediction. Experiments show that CoLa-ICD improves long-tail prediction with larger gains in larger and sparser label spaces and achieves state-of-the-art performance in AUC, F1, and P@k. Our code is available at https://github.com/youwillbethebest/Cola-ICD.</abstract>
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%0 Conference Proceedings
%T CoLa-ICD: A Knowledge-Enhanced Framework for Long-Tail Automated Medical Coding
%A Cheng, Yihang
%A Liesaputra, Veronica
%A Trotman, Andrew
%Y Lee, Hung-Yi
%Y Dev, Sunipa
%Y Li, Jessy
%Y Elliott, Desmond
%S Findings of the Association for Computational Linguistics: EMNLP 2026
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-462-0
%F cheng-etal-2026-cola
%X Automatic medical coding assigns ICD codes to clinical notes, but it remains challenging due to long documents, imbalanced label distributions, and diverse terms. These challenges are especially severe for rare codes, which have limited training instances and are easily confused with semantically similar labels. We introduce **CoLa-ICD**, a knowledge-enhanced framework for long-tail prediction. CoLa-ICD enriches ICD labels with external terms, models dependencies among related codes, and learns stronger alignment between label semantics and clinical evidence for long-tail prediction. Experiments show that CoLa-ICD improves long-tail prediction with larger gains in larger and sparser label spaces and achieves state-of-the-art performance in AUC, F1, and P@k. Our code is available at https://github.com/youwillbethebest/Cola-ICD.
%U https://aclanthology.org/2026.findings-emnlp.731/
%P 15368-15380

Markdown (Informal)

[CoLa-ICD: A Knowledge-Enhanced Framework for Long-Tail Automated Medical Coding](https://aclanthology.org/2026.findings-emnlp.731/) (Cheng et al., Findings 2026)

• CoLa-ICD: A Knowledge-Enhanced Framework for Long-Tail Automated Medical Coding (Cheng et al., Findings 2026)

ACL

• Yihang Cheng, Veronica Liesaputra, and Andrew Trotman. 2026. CoLa-ICD: A Knowledge-Enhanced Framework for Long-Tail Automated Medical Coding. In Findings of the Association for Computational Linguistics: EMNLP 2026, pages 15368–15380, Budapest, Hungary. Association for Computational Linguistics.