利用大型语言模型提取和翻译医生笔记中的医疗信息用于健康记录和诊断收费代码
Leveraging Large Language Models to Extract and Translate Medical Information in Doctors' Notes for Health Records and Diagnostic Billing Codes
AI总结：
本文研究利用本地大语言模型提取医生笔记中的临床信息并翻译为ICD-10-CM诊断代码，探讨隐私保护下的医疗信息提取方法，发现需结合人类辅助以提高准确性。
摘要：
美国医生倦怠已达到临界水平，部分由电子健康记录（EHR）文档的行政负担和复杂诊断代码驱动。本论文探索了一种在设备上离线自动医疗编码系统。研究重点是利用开源大语言模型（LLMs）从医生笔记中提取临床信息并翻译为ICD-10-CM诊断代码，而无需依赖云服务。开发了一个专注于隐私的流水线，使用Ollama、LangChain和容器化环境评估多个开源模型，包括Llama 3.2、Mistral、Phi和DeepSeek，在消费级硬件上。模型性能在零样本、少样本和检索增强生成（RAG）提示策略下通过新的合成医疗笔记基准进行评估。结果表明，严格的JSON模式强制执行实现了近100%的格式合规性，但生成特定诊断代码的准确性仍对较小的本地模型（7B-20B参数）具有挑战性。与常见的提示工程指导相反，少样本提示通过过拟合和幻觉降低了性能。虽然RAG能够有限地发现未见过的代码，但经常饱和上下文窗口，降低了整体准确性。研究发现表明，完全自动化无监督编码使用本地开源模型尚不可靠；目前最实用的路径是结合人类辅助的编码方法。本工作贡献了一种可重复的本地LLM架构和基准数据集，用于隐私保护的医疗信息提取和编码。
英文摘要：
Physician burnout in the United States has reached critical levels, driven in part by the administrative burden of Electronic Health Record (EHR) documentation and complex diagnostic codes. To relieve this strain and maintain strict patient privacy, this thesis explores an on-device, offline automatic medical coding system. The work focuses on using open-weight Large Language Models (LLMs) to extract clinical information from physician notes and translate it into ICD-10-CM diagnostic codes without reliance on cloud-based services. A privacy-focused pipeline was developed using Ollama, LangChain, and containerized environments to evaluate multiple open-weight models, including Llama 3.2, Mistral, Phi, and DeepSeek, on consumer-grade hardware. Model performance was assessed for zero-shot, few-shot, and retrieval-augmented generation (RAG) prompting strategies using a novel benchmark of synthetic medical notes. Results show that strict JSON schema enforcement achieved near 100% formatting compliance, but accurate generation of specific diagnostic codes remains challenging for smaller local models (7B-20B parameters). Contrary to common prompt-engineering guidance, few-shot prompting degraded performance through overfitting and hallucinations. While RAG enabled limited discovery of unseen codes, it frequently saturated context windows, reducing overall accuracy. The findings suggest that fully automated unsupervised coding with local open-source models is not yet reliable; instead, a human-in-the-loop assisted coding approach is currently the most practical path forward. This work contributes a reproducible local LLM architecture and benchmark dataset for privacy-preserving medical information extraction and coding.
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