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ICDAGENT: Empowering Agentic Large Language Models for Explainable Medical Coding

Paper · 2026 · Ziyi Yin, Yuanpu Cao, Ting Wang et al.

Summary

The paper addresses explainable ICD coding: assigning International Classification of Diseases codes to clinical notes and justifying each code in free text. The authors argue that general-purpose commercial LLMs perform poorly here because they lack coding expertise and must navigate a huge label space of closely related codes. They propose ICDAGENT, which pairs two fine-tuned open models. A coding agent proposes codes with draft justifications. A critical agent reasons step by step to accept or reject each draft. To train the coding agent, the authors automatically built a rationale dataset from MIMIC-IV notes and official code descriptions. The critical agent first learns chain-of-thought from a proprietary teacher, then undergoes reinforcement learning with a reward that accounts for rationale quality. The two agents interact over several rounds. Across three datasets covering ICD-9 and ICD-10, ICDAGENT beats commercial LLMs and runs locally on two A100 GPUs. Local deployment lowers cost and avoids privacy risks.

Key points

  • Coding agent: HuatuoGPT-o1-8B is LoRA-fine-tuned on the new D_code dataset. The dataset was built from MIMIC-IV in two automated steps: an open-source LLM extracts a rationale for each gold code, then labels are aggregated.
  • Critical agent: Qwen3-4B is LoRA-tuned on CoT trajectories distilled from Gemini. It is then optimized via RL with a rationale-aware reward. Rationales for incorrect codes are regenerated.
  • Baselines: The commercial LLMs compared include GPT-4o, GPT-5-Chat, Claude-3.7/4-Sonnet, and Gemini-2.5-Pro/Flash. Evaluation uses MIMIC-III and MIMIC-IV, with predicted codes extracted by string matching on delimited tokens such as |E11.9|.
  • Comparison with CoRelation: On micro-level metrics, where frequent codes carry most of the weight, ICDAGENT falls behind the classifier CoRelation. It outperforms CoRelation on macro-level metrics, indicating better handling of rare, long-tail codes.
  • Stated limitations: Recall still lags behind state-of-the-art classifiers. Training also requires costly annotation, including commercial API use. The authors suggest future work on enriched code guidelines combined with RAG.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Pennsylvania State UniversityState College · United States67 works · 5,865 citations

 Fields, topics and concepts

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 18,614 worksArtificial Intelligence · 17,965 worksBiochemistry, Genetics and Molecular Biology · 263 worksMolecular Biology · 260 worksHealth Professions · 75 worksHealth Information Management · 56 works

Cite

Cite as Ziyi Yin, Yuanpu Cao, Ting Wang, Jinghui Chen and Fenglong Ma. 2026. ICDAGENT: Empowering Agentic Large Language Models for Explainable Medical Coding. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers).

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.18653/v1/2026.acl-long.643

@inproceedings{yin2026icdagent,
  title = {{ICDAGENT: Empowering Agentic Large Language Models for Explainable Medical Coding}},
  author = {Ziyi Yin, Yuanpu Cao, Ting Wang, Jinghui Chen and Fenglong Ma},
  booktitle = {Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  year = {2026},
  doi = {10.18653/v1/2026.acl-long.643},
  url = {https://doi.org/10.18653/v1/2026.acl-long.643}
}

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