@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://aclanthology.org/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)."
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<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).</abstract>
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%0 Conference Proceedings
%T Code Like Humans: A Multi-Agent Solution for Medical Coding
%A Motzfeldt, Andreas Geert
%A Edin, Joakim
%A Christensen, Casper L.
%A Hardmeier, Christian
%A Maaløe, Lars
%A Rogers, Anna
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Findings of the Association for Computational Linguistics: EMNLP 2025
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-335-7
%F motzfeldt-etal-2025-code
%X 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).
%U https://aclanthology.org/2025.findings-emnlp.1231/
%P 22612-22627
Markdown (Informal)
[Code Like Humans: A Multi-Agent Solution for Medical Coding](https://aclanthology.org/2025.findings-emnlp.1231/) (Motzfeldt et al., Findings 2025)
ACL
- Andreas Geert Motzfeldt, Joakim Edin, Casper L. Christensen, Christian Hardmeier, Lars Maaløe, and Anna Rogers. 2025. Code Like Humans: A Multi-Agent Solution for Medical Coding. In Findings of the Association for Computational Linguistics: EMNLP 2025, pages 22612–22627, Suzhou, China. Association for Computational Linguistics.