用大模型模拟医生编码，支持超7万种疾病代码。
Code Like Humans: A Multi-Agent Solution for Medical Coding
- 构建多智能体系统，遵循官方编码规范。
- 首次实现完整ICD-10系统覆盖（+7万标签），罕见病编码性能最优。
- 发现系统盲区：部分代码被系统性漏编，适合医疗信息化研究者。
在医疗编码中，专家需将非结构化临床笔记映射为诊断与操作的字母数字代码。我们提出Code Like Humans：一种基于大语言模型的新型代理框架，遵循人类专家的官方编码指南，是首个支持完整ICD-10编码体系（+7万标签）的解决方案。其在罕见诊断代码上的表现优于现有方法（微调判别分类器在高频代码上仍有优势，但受限于训练数据）。未来工作中，我们还分析了系统性能并识别出其‘盲点’——存在系统性漏编的代码，为改进提供方向。
原文摘要 · Abstract (English)
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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