利用隐私保护的合成临床数据训练大型语言模型进行医学编码
Training a Large Language Model for Medical Coding Using Privacy-Preserving Synthetic Clinical Data
AI总结：
本文研究了利用隐私保护的合成临床数据训练大型语言模型进行医学编码的可行性，通过微调Llama 3-70B模型，实现了ICD-10-CM和CPT代码的高精度预测，显著提升了编码准确性。
摘要：
提高医学编码的准确性和可靠性可以减少医护人员疲劳，支持收入周期流程，使医护人员能更专注于患者护理。然而，从临床文档中自动化分配ICD-10-CM和CPT代码仍具挑战性，由于记录异质性、编码指南复杂性和长尾分布。大型语言模型已被提出帮助或自动化特定医学编码任务。然而，基础模型并未专门训练用于医学编码，零样本编码效果不佳。我们研究了现代开放式基础模型是否能通过隐私保护的合成训练数据（源自电子健康记录）适应专家级医学编码任务。我们对Llama 3-70B进行微调，使用临床笔记和金标准代码对（由EHR基础模板和编码政策生成），然后评估ICD-10-CM和CPT的精确代码预测。未适应模型的零样本基线在精确代码匹配上的F1得分为0.18。在合成语料库上微调后，精确匹配F1超过0.70，代表在两种代码系统上均实现了显著的绝对提升。值得注意的是，性能在需要多步骤临床推理和代码组合的复杂类别（如高级疾病和脆弱类别）中仍保持较高水平，且模型在医疗理解任务上保持了其性能。这些结果表明，合成、基于政策的数据可以高效地教给通用大型语言模型以支持精确的医学编码，而无需暴露受保护的健康信息。该方法为安全地在特定任务上训练编码代理提供了可行的路径，这些任务代表现实世界的人群。
英文摘要：
Improving the accuracy and reliability of medical coding reduces clinician burnout and supports revenue cycle processes, freeing providers to focus more on patient care. However, automating the assignment of ICD-10-CM and CPT codes from clinical documentation remains a challenge due to heterogeneous records, nuanced coding guidelines, and long-tail distributions. Large language models have been proposed to help or automate specific medical coding tasks. However, foundation models are not explicitly trained for medical coding and zero-shot coding has yielded poor results. We investigate whether a modern open-weight foundation model can be adapted for an expert-level medical coding task using privacy-preserving synthetic training data derived from electronic health records. We fine-tune Llama 3-70B on pairs of clinical notes and gold codes generated from EHR-grounded templates and coding policies, then evaluate exact-code prediction for ICD-10-CM and CPT. A zero-shot baseline with the unadapted model achieved an F1 score of 0.18 for exact code match. After fine-tuning on the synthetic corpus, exact-match F1 exceeded 0.70, representing a large absolute gain across both code systems. Notably, performance remained high on complex categories that often require multi-step clinical reasoning and code composition, including Advanced Illness and Frailty classes, and the model retained its performance on medical comprehension tasks. These results indicate that synthetic, policy-aware data can efficiently teach a general-purpose large language model to support precise medical coding without exposing protected health information. The approach offers a practical path for training coding agents safely and iteratively on specific tasks that represent real-world populations.
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