Automated ICD coding is a critical task for standardizing clinical information from electronic health records (EHRs) and supporting downstream healthcare administration.However, existing automated ICD coding systems face several fundamental challenges. First, the majority of existing research focuses on English ICD tasks, with limited attention to Chinese-language clinical contexts due to the scarcity of publicly available Chinese ICD datasets. Second, most approaches primarily target disease coding, overlooking procedure coding as well as the multi-stage workflows followed in real-world clinical practice. Moreover, many recent methods rely heavily on closed-source large language models or substantial computational resources, which limits their scalability and deployability in clinical environments.To address these gaps, this paper proposes JointCoder, which includes a real-world Chinese ICD coding dataset and a multi-agent framework that reformulates automated ICD coding as a joint disease-procedure coding task. JointCoder explicitly models real-world clinical coding workflows through stage-wise agent collaboration.All agents are instantiated using locally deployed 1.7B-parameter models, enabling scalable and privacy-preserving deployment.Extensive experiments on real-world Chinese ICD coding datasets demonstrate JointCoder’s superiority over state-of-the-art baselines across all evaluation metrics.

"中文电子病历国际疾病分类(ICD)诊断编码评测依托第二十四届中国计算语言学大会(CCL)举办。该评测聚焦于自然语言处理技术在智能医疗领域的应用,旨在从真实脱敏的电子病历文本中自动分析关键临床表征,实现主诊断及其他诊断ICD编码的精准预测与分配,从而辅助临床医生与专业编码员提升编码工作的准确性和效率。本次评测在阿里云天池平台进行,获得了学术界与工业界的广泛关注和积极参与。数据显示,共有445支队伍报名参赛,其中A榜和B榜分别有85支和36支队伍成功提交了有效结果。最终,8支表现优异的队伍受邀撰写并分享了其技术报告,为推动该领域的技术进步与方法创新贡献了宝贵经验。本次评测的详细信息可参见相关发布页面。"