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    <abstract>“With the widespread adoption of Electronic Medical Records (EMRs), automated coding of theInternational Classification of Diseases (ICD) has become increasingly essential. However, the complexity of Chinese clinical texts presents significant challenges to traditional methods. To address these issues, CCL25-Eval Task 8 organized the Chinese EMRs ICD Diagnosis CodingEvaluation. This paper presents a method based on Large Language Models (LLMs), which divides the task into primary and other diagnosis coding. For the primary diagnosis, a confidence-guided semantic retrieval strategy is applied, while ensemble learning enhanced with NamedEntity Recognition (NER) is used for other diagnoses. The proposed approach achieved 83.42%accuracy on the official test set, ranking second in the evaluation.”</abstract>
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%0 Conference Proceedings
%T System Report for CCL25-Eval Task 8: Improving ICD Coding with Large Language Models via Disease Entity Recognition
%A Lv, Tengxiao
%A Li, Juntao
%A Liu, Chao
%A Yuan, Haobin
%A Luo, Ling
%A Wang, Jian
%A Lin, Hongfei
%Y Lin, Hongfei
%Y Li, Bin
%Y Tan, Hongye
%S Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025)
%D 2025
%8 August
%I Chinese Information Processing Society of China
%C Jinan, China
%F lv-etal-2025-system
%X “With the widespread adoption of Electronic Medical Records (EMRs), automated coding of theInternational Classification of Diseases (ICD) has become increasingly essential. However, the complexity of Chinese clinical texts presents significant challenges to traditional methods. To address these issues, CCL25-Eval Task 8 organized the Chinese EMRs ICD Diagnosis CodingEvaluation. This paper presents a method based on Large Language Models (LLMs), which divides the task into primary and other diagnosis coding. For the primary diagnosis, a confidence-guided semantic retrieval strategy is applied, while ensemble learning enhanced with NamedEntity Recognition (NER) is used for other diagnoses. The proposed approach achieved 83.42%accuracy on the official test set, ranking second in the evaluation.”
%U https://aclanthology.org/2025.ccl-2.36/
%P 304-311

Markdown (Informal)

[System Report for CCL25-Eval Task 8: Improving ICD Coding with Large Language Models via Disease Entity Recognition](https://aclanthology.org/2025.ccl-2.36/) (Lv et al., CCL 2025)

• System Report for CCL25-Eval Task 8: Improving ICD Coding with Large Language Models via Disease Entity Recognition (Lv et al., CCL 2025)

ACL

• Tengxiao Lv, Juntao Li, Chao Liu, Haobin Yuan, Ling Luo, Jian Wang, and Hongfei Lin. 2025. System Report for CCL25-Eval Task 8: Improving ICD Coding with Large Language Models via Disease Entity Recognition. In Proceedings of the 24th China National Conference on Computational Linguistics (CCL 2025), pages 304–311, Jinan, China. Chinese Information Processing Society of China.