A General Knowledge Injection Framework for ICD Coding
Zhang, Xu Zhang, Kun Ma, Wenxin Wang, Rongsheng Wu, Chenxu Li, Yingtai Zhou, S Kevin
Zhang, Xu
Zhang, Kun
Ma, Wenxin
Wang, Rongsheng
Wu, Chenxu
Li, Yingtai
Zhou, S Kevin
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
License
Language
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
ICD Coding aims to assign a wide range of medical codes to a medical text document, which is a popular and challenging task in the healthcare domain. To alleviate the problems of long-tail distribution and the lack of annotations of code-specific evidence, many previous works have proposed incorporating code knowledge to improve coding performance. However, existing methods often focus on a single type of knowledge and design specialized modules that are complex and incompatible with each other, thereby limiting their scalability and effectiveness. To address this issue, we propose GKI-ICD, a novel, general knowledge injection framework that integrates three key types of knowledge, namely ICD Description, ICD Synonym, and ICD Hierarchy, without specialized design of additional modules. The comprehensive utilization of the above knowledge, which exhibits both differences and complementarity, can effectively enhance the ICD coding performance. Extensive experiments on existing popular ICD coding benchmarks demonstrate the effectiveness of GKI-ICD, which achieves the state-of-the-art performance on most evaluation metrics. Code is available at https://github.com/xuzhang0112/GKI-ICD.
Citation
X. Zhang, K. Zhang, W. Ma, R. Wang, C. Wu, Y. Li , et al., "A General Knowledge Injection Framework for ICD Coding," 2025, pp. 7180-7189.
Source
Conference
Findings of the Association for Computational Linguistics: ACL 2025
Keywords
Subjects
Source
Findings of the Association for Computational Linguistics: ACL 2025
Publisher
Association for Computational Linguistics