JointCoder: Exploring Automated ICD Coding on Real-World Chinese EHRs with a Multi-Agent Framework
Year: 2026
Type: other
Abstract: 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 \textit{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. Our Github repository is available at https://github.com/KJOrigin/JointCoder. Our demo website is available at http://159.89.222.214:9028/. The demo video can be accessed at https://yunpan.qlu.edu.cn:443/link/CE86D849DD24EA9EE3D68AF2FD90D539.
Source: Underline Science Inc.
DOI: 10.48448/6xn0-x387
Institutions: Qilu University of Technology, Shandong Academy of Sciences
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7168564453