用大模型精准查询病历数据并提取文本信息，提升临床数据分析效率。
Harnessing Large Language Models for Precision Querying and Retrieval-Augmented Knowledge Extraction in Clinical Data Science
- 结合程序化查询与RAG管道，让大模型理解复杂医疗数据。
- 在MIMIC-III数据上实现高精度问答，准确率显著优于基线。
- 适合医疗数据科学家、临床研究者快速构建智能分析工具。
本研究将大语言模型（LLMs）应用于电子健康记录（EHR）数据科学中的两项基础任务：使用编程语言（如Python/Pandas）进行结构化数据查询，以及通过检索增强生成（RAG）管道从非结构化临床文本中提取信息。评估了大模型在大型结构化数据集上进行精确交互的能力，以及在RAG支持下从自由文本病历中提取语义正确信息的可靠性。为此，我们设计了一个灵活的评估框架，可自动生成针对特定数据集或任务特征的合成问答对。实验基于MIMIC-III的精选子集（包含四个结构化表和一种临床笔记类型），使用本地部署与API调用的大模型混合方案。评估综合采用精确匹配、语义相似度与人工判断。结果表明，大模型在临床工作流中具备支持精准查询与准确信息提取的潜力。
原文摘要 · Abstract (English)
This study applies Large Language Models (LLMs) to two foundational Electronic Health Record (EHR) data science tasks: structured data querying (using programmatic languages, Python/Pandas) and information extraction from unstructured clinical text via a Retrieval Augmented Generation (RAG) pipeline. We test the ability of LLMs to interact accurately with large structured datasets for analytics and the reliability of LLMs in extracting semantically correct information from free text health records when supported by RAG. To this end, we presented a flexible evaluation framework that automatically generates synthetic question and answer pairs tailored to the characteristics of each dataset or task. Experiments were conducted on a curated subset of MIMIC III, (four structured tables and one clinical note type), using a mix of locally hosted and API-based LLMs. Evaluation combined exact-match metrics, semantic similarity, and human judgment. Our findings demonstrate the potential of LLMs to support precise querying and accurate information extraction in clinical workflows.
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