融合三种医学编码知识，提升疾病编码准确率
A General Knowledge Injection Framework for ICD Coding
- 统一框架整合编码描述、同义词和层级关系知识
- 在多个基准上达到当前最佳性能，有效缓解长尾分布问题
- 无需额外模块设计，适合医疗文本标注场景
ICD编码旨在为医疗文本分配多种医学编码，是医疗领域常见且具有挑战性的任务。针对编码分布不均和特定编码证据标注不足的问题，现有方法多引入代码知识以提升性能。然而，多数方法仅聚焦单一类型知识，设计复杂且互不兼容的专用模块，限制了可扩展性与效果。为此，我们提出GKI-ICD——一种通用知识注入框架，无需额外模块设计，即可集成ICD描述、同义词与层级关系三类关键知识。这三类知识具有差异性与互补性，能有效提升编码性能。在多个主流ICD编码基准上的大量实验表明，GKI-ICD在多数评估指标上达到当前最优表现。代码已开源：https://github.com/xuzhang0112/GKI-ICD。
原文摘要 · Abstract (English)
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.
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