{"ID":2874705,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.05378","arxiv_id":"2509.05378","title":"Code Like Humans: A Multi-Agent Solution for Medical Coding","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).","short_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 t...","url_abs":"https://arxiv.org/abs/2509.05378","url_pdf":"https://arxiv.org/pdf/2509.05378v3","authors":"[\"Andreas Motzfeldt\",\"Joakim Edin\",\"Casper L. Christensen\",\"Christian Hardmeier\",\"Lars Maaløe\",\"Anna Rogers\"]","published":"2025-09-04T16:31:38Z","proceeding":"cs.AI","tasks":"[\"cs.AI\",\"cs.MA\"]","methods":"[\"Language Model\"]","has_code":false}