@inproceedings{motzfeldt-etal-2025-code,
    title = "Code Like Humans: A Multi-Agent Solution for Medical Coding",
    author = "Motzfeldt, Andreas Geert  and
      Edin, Joakim  and
      Christensen, Casper L.  and
      Hardmeier, Christian  and
      Maal{\o}e, Lars  and
      Rogers, Anna",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2025",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/author-page-yu-wang-polytechnic/2025.findings-emnlp.1231/",
    doi = "10.18653/v1/2025.findings-emnlp.1231",
    pages = "22612--22627",
    ISBN = "979-8-89176-335-7",
    abstract = "In medical coding, experts map unstructured clinical notes to alphanumeric codes for diagnoses and procedures. We introduce `Code Like Humans': a new agentic framework for medical coding with large language models. It implements official coding guidelines for human experts, and it is the first solution that can support the full ICD-10 coding system (+70K labels). It achieves the best performance to date on rare diagnosis codes. Fine-tuned discriminative classifiers retain an advantage for high-frequency codes, to which they are limited. Towards future work, we also contribute an analysis of system performance and identify its `blind spots' (codes that are systematically undercoded)."
}