@inproceedings{lv-etal-2025-system,
    title = "System Report for {CCL}25-Eval Task 8: Improving {ICD} Coding with Large Language Models via Disease Entity Recognition",
    author = "Lv, Tengxiao  and
      Li, Juntao  and
      Liu, Chao  and
      Yuan, Haobin  and
      Luo, Ling  and
      Wang, Jian  and
      Lin, Hongfei",
    editor = "Lin, Hongfei  and
      Li, Bin  and
      Tan, Hongye",
    booktitle = "Proceedings of the 24th {C}hina National Conference on Computational Linguistics ({CCL} 2025)",
    month = aug,
    year = "2025",
    address = "Jinan, China",
    publisher = "Chinese Information Processing Society of China",
    url = "https://preview.aclanthology.org/ingest-ccl/2025.ccl-2.36/",
    pages = "304--311",
    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.''"
}