@inproceedings{wang-etal-2025-system,
    title = "System Report for {CCL}25-Eval Task 8: Structured {ICD} Coding with {LLM}-Augmented Learning and Group-specific Classifiers",
    author = "Wang, Bo  and
      Zhang, Kaiyuan  and
      Feng, Chong  and
      Shi, Ge  and
      Ye, Jinhua  and
      Teng, Jiahao  and
      Wang, Shouzhen  and
      Meng, Fanqing  and
      Yuan, Changsen  and
      Zhuang, Yan",
    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-2.37/",
    pages = "312--321",
    abstract = "``The International Classification of Diseases (ICD) provides a standardized framework for encoding diagnoses, serving critical roles in clinical scenarios. Automatic ICD coding aims to assign formalized diagnostic codes to medical records for documentation and analysis, which is challenged by an extremely large and imbalanced label space, noisy and heterogeneous clinical text,and the need for interpretability. In this paper, we propose a structured multi-class classification framework that partitions diseases into clinically coherent groups, enabling group-specific dataaugmentation and supervision. Our method combines input compression with generative and discriminative fine-tuning strategies tailored to primary and secondary diagnoses, respectively.On the CCL2025-Eval Task 8 benchmark for Chinese electronic medical records, our approach ranked first in the final evaluation.''"
}