[基于LLM的临床数据提取:潜力和挑战]
Paulina Seidl1, Marton Szep2,3, Sebastian Breden2
1Klinik und Poliklinik für Orthopädie und Sportorthopädie, TUM Universitätsklinikum, Ismaninger Str. 22, 81675, München, Deutschland. paulina.seidl@tum.de.
Orthopadie (Heidelberg, Germany)
|December 19, 2025
概括
大型语言模型 (LLM) 可以从临床文本中提取信息,减少工作量. 快速质量显著影响LLM准确性,以获得可靠的临床数据提取.
科学领域:
- 医疗信息学 医疗信息学
- 人工智能在医学中的应用
背景情况:
- 大型语言模型 (LLM) 提供了临床自由文本文档的自动处理.
- 法律学可以减少医疗保健专业人员的阅读力度和文档工作负担.
- 高质量的数据和精确的模型控制对于LLM在医疗保健中的实际应用至关重要.
研究的目的:
- 用本地LLM评估信息提取不同提示策略的有效性.
- 以不同的提示来评估从病理报告中提取的数据的质量.
- 确定在临床环境中实施LLM的挑战和最佳实践.
主要方法:
- 使用本地,通用的LLM (Llama 4 Scout) 来处理24个非结构化的病理报告.
- 采用了三个不同的提示符变体来测试信息提取能力.
- 在不同的提示符设计中比较了提取质量.
主要成果:
- 快速设计极大地影响了LLM的性能和输出可靠性.
- 有明确参数定义和示例的提示结果产生了最一致的结果.
- 观察到常见的LLM错误,包括幻觉和错误分类,需要仔细验证.
结论:
- 通过系统地从自由文本文档中提取相关信息,LLM显示出帮助临床工作人员的潜力.
- 有效和安全的LLM部署需要高质量的数据,精确的输入参数和跨学科的合作.
- 对快速工程和错误减轻的进一步研究对于临床LLM整合至关重要.
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