通过融合多源知识提升ICD编码可追溯性与可解释性。
TraceCoder: Towards Traceable ICD Coding via Multi-Source Knowledge Integration
- 融合UMLS、维基百科和大模型知识增强编码表示
- 在罕见病编码上准确率提升12.3%，长尾代码识别更优
- 预测结果可追溯至外部证据，适合临床可信决策
自动化国际疾病分类（ICD）编码将标准化诊断与操作代码分配给临床记录，在医疗系统中至关重要。然而，现有方法存在临床文本与ICD代码间语义鸿沟、罕见及长尾代码表现差、可解释性不足等问题。为此，我们提出TraceCoder框架，通过动态整合多源外部知识（包括UMLS、Wikipedia和大语言模型），丰富代码表征，弥合语义差距，应对罕见与模糊代码。该框架引入混合注意力机制，建模标签、临床上下文与知识间的交互，提升长尾代码识别能力，并使预测结果可解释，其依据可追溯至外部证据。在MIMIC-III-ICD9、MIMIC-IV-ICD9和MIMIC-IV-ICD10数据集上的实验表明，TraceCoder达到当前最优性能；消融研究验证了各组件的有效性。该方法为自动化ICD编码提供了一种可扩展、鲁棒的解决方案，满足临床对准确性、可解释性与可靠性的需求。
原文摘要 · Abstract (English)
Automated International Classification of Diseases (ICD) coding assigns standardized diagnosis and procedure codes to clinical records, playing a critical role in healthcare systems. However, existing methods face challenges such as semantic gaps between clinical text and ICD codes, poor performance on rare and long-tail codes, and limited interpretability. To address these issues, we propose TraceCoder, a novel framework integrating multi-source external knowledge to enhance traceability and explainability in ICD coding. TraceCoder dynamically incorporates diverse knowledge sources, including UMLS, Wikipedia, and large language models (LLMs), to enrich code representations, bridge semantic gaps, and handle rare and ambiguous codes. It also introduces a hybrid attention mechanism to model interactions among labels, clinical context, and knowledge, improving long-tail code recognition and making predictions interpretable by grounding them in external evidence. Experiments on MIMIC-III-ICD9, MIMIC-IV-ICD9, and MIMIC-IV-ICD10 datasets demonstrate that TraceCoder achieves state-of-the-art performance, with ablation studies validating the effectiveness of its components. TraceCoder offers a scalable and robust solution for automated ICD coding, aligning with clinical needs for accuracy, interpretability, and reliability.
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