ICDAGENT: Empowering Agentic Large Language Models for Explainable Medical Coding (2026.acl-long)
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| Challenge: | Existing models lack convincing, human-understandable explanations, making them difficult for physicians to trust and use in practice. |
| Approach: | They propose a framework that aims to automatically assign ICD codes to clinical notes while providing explicit justifications for each assignment. |
| Outcome: | The proposed framework achieves effective ICD coding with accurate explanations using two collaborative LLM agents: a coding agent and a critical agent. |
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| Challenge: | Current efforts in interpretability of medical coding rely heavily on label attention mechanisms, which often leads to the highlighting of extraneous tokens irrelevant to the ICD code. |
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| Challenge: | Existing studies on the explainability of ICD coding rely on attention-based rationales and qualitative assessments conducted by physicians. |
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Andreas Geert Motzfeldt, Joakim Edin, Casper L. Christensen, Christian Hardmeier, Lars Maaløe, Anna Rogers
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| Challenge: | Existing approaches to assigning ICD codes to clinical text are time-consuming, labor intensive, and error-prone. |
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| Challenge: | Existing methods of automatic coding prediction have been successful, but the interpretability of predicted codes is a challenge. |
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| Challenge: | Existing automated ICD coding systems face several fundamental challenges due to the limited availability of publicly available Chinese ICD datasets. |
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| Challenge: | Clinical coding is labor-intensive and prone to delays, leading to global backlogs. |
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CoRelation: Boosting Automatic ICD Coding through Contextualized Code Relation Learning (2024.lrec-main)
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| Challenge: | Existing methods for boosting ICD coding performance lack a model for complex code relations . current methods overlook the importance of context in clinical notes . |
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