用小模型生成临床试验匹配查询，效果媲美专家且高效隐私。
Leveraging Large Language Models for Medical Information Extraction and Query Generation
- 用开源小模型自动生成患者匹配试验的查询语句。
- 生成查询召回率超专家水平，响应时间1.7~8秒。
- 适合医疗场景落地，兼顾效率与隐私保护。
本文提出一种将大语言模型（LLMs）融入临床试验检索系统的方案，提升患者与合适试验的匹配效率，同时保障信息隐私并支持专家监督。我们评估了六种LLM在查询生成中的表现，包括两种闭源和四种开源模型，其中一为医学专用模型，五为通用模型。对比了这些模型生成的查询与医学专家及文献中先进方法的效果。结果表明，所评估模型的检索效果达到或超过专家水平，显著优于标准基线和其他已有方法。表现最佳的模型响应时间仅1.7至8秒，平均生成15至63个查询词，具备实际应用可行性。研究显示，使用小型开源LLM可实现性能、计算效率与临床实用性的良好平衡。
原文摘要 · Abstract (English)
This paper introduces a system that integrates large language models (LLMs) into the clinical trial retrieval process, enhancing the effectiveness of matching patients with eligible trials while maintaining information privacy and allowing expert oversight. We evaluate six LLMs for query generation, focusing on open-source and relatively small models that require minimal computational resources. Our evaluation includes two closed-source and four open-source models, with one specifically trained in the medical field and five general-purpose models. We compare the retrieval effectiveness achieved by LLM-generated queries against those created by medical experts and state-of-the-art methods from the literature. Our findings indicate that the evaluated models reach retrieval effectiveness on par with or greater than expert-created queries. The LLMs consistently outperform standard baselines and other approaches in the literature. The best performing LLMs exhibit fast response times, ranging from 1.7 to 8 seconds, and generate a manageable number of query terms (15-63 on average), making them suitable for practical implementation. Our overall findings suggest that leveraging small, open-source LLMs for clinical trials retrieval can balance performance, computational efficiency, and real-world applicability in medical settings.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。