利用LLMs从非结构化患者记录中提取结构化数据
Leveraging LLMs for Structured Data Extraction from Unstructured Patient Records
AI总结：
本文提出利用本地部署的LLMs，通过RAG和结构化响应方法，实现从非结构化患者记录中高效提取结构化数据，提升临床研究效率。
摘要：
手动病历审查仍然是临床研究中极其耗时且资源密集的组成部分，需要专家从非结构化的电子健康记录（EHR）叙述中提取往往复杂的 Informationen。我们提出一个安全、模块化的框架，利用本地部署的大型语言模型（LLMs）在机构批准的、符合《健康保险流通与责任法案》（HIPAA）的计算基础设施上，实现自动化结构化特征提取。该系统将检索增强生成（RAG）和LLMs的结构化响应方法整合到一个广泛部署且可扩展的容器中，以提供多种临床领域的特征提取。在评估中，该框架在多个医疗特征上实现了高准确性，与专家标注数据集相比，识别出几个在手动审查中被遗漏的标注错误。该框架展示了LLM系统通过自动化提取减少手动病历审查负担的潜力，并提高数据采集的一致性，加速临床研究。
英文摘要：
Manual chart review remains an extremely time-consuming and resource-intensive component of clinical research, requiring experts to extract often complex information from unstructured electronic health record (EHR) narratives. We present a secure, modular framework for automated structured feature extraction from clinical notes leveraging locally deployed large language models (LLMs) on institutionally approved, Health Insurance Portability and Accountability Act (HIPPA)-compliant compute infrastructure. This system integrates retrieval augmented generation (RAG) and structured response methods of LLMs into a widely deployable and scalable container to provide feature extraction for diverse clinical domains. In evaluation, the framework achieved high accuracy across multiple medical characteristics present in large bodies of patient notes when compared against an expert-annotated dataset and identified several annotation errors missed in manual review. This framework demonstrates the potential of LLM systems to reduce the burden of manual chart review through automated extraction and increase consistency in data capture, accelerating clinical research.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
