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TraceCoder: Towards Traceable ICD Coding via Multi-Source Knowledge Integration

arXiv — cs.CL•Wednesday, November 12, 2025 at 5:00:00 AM
PositiveArtificial Intelligence
The introduction of TraceCoder marks a significant advancement in automated ICD coding, a critical component of healthcare systems that assigns standardized diagnosis and procedure codes to clinical records. Existing methods often struggle with semantic gaps between clinical text and ICD codes, particularly for rare and long-tail codes. TraceCoder addresses these issues by integrating diverse knowledge sources such as UMLS, Wikipedia, and large language models, enhancing the traceability and explainability of ICD coding. Its hybrid attention mechanism improves the recognition of long-tail codes and grounds predictions in external evidence, making them more interpretable. Experiments conducted on MIMIC-III-ICD9, MIMIC-IV-ICD9, and MIMIC-IV-ICD10 datasets demonstrate that TraceCoder achieves state-of-the-art performance, validating its effectiveness and scalability. This innovation is crucial for improving the accuracy of medical coding, which directly impacts healthcare delivery and out…
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