Code Like Humans: A Multi-Agent Solution for Medical Coding
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911196740845568 |
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| author | Motzfeldt, Andreas Edin, Joakim Christensen, Casper L. Hardmeier, Christian Maaløe, Lars Rogers, Anna |
| author_facet | Motzfeldt, Andreas Edin, Joakim Christensen, Casper L. Hardmeier, Christian Maaløe, Lars Rogers, Anna |
| contents | 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). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_05378 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Code Like Humans: A Multi-Agent Solution for Medical Coding Motzfeldt, Andreas Edin, Joakim Christensen, Casper L. Hardmeier, Christian Maaløe, Lars Rogers, Anna Artificial Intelligence Multiagent Systems 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). |
| title | Code Like Humans: A Multi-Agent Solution for Medical Coding |
| topic | Artificial Intelligence Multiagent Systems |
| url | https://arxiv.org/abs/2509.05378 |