利用大型语言模型来提取一般和特定领域的符号和症状
C Mahony Reategui-Rivera1, Stefan Escobar-Agreda2, Joseph Finkelstein1
1Department of Biomedical Informatics, School of Medicine, University of Utah, Salt Lake City, Utah.
Studies in health technology and informatics
|July 1, 2025
概括
像GPT-4o这样的大型语言模型显示出从临床笔记中提取标志和症状 (S&S) 的希望. GPT-4o实现了尿路特异性S&S提取的高性能,证明了其在分析电子健康记录方面的潜力.
科学领域:
- 自然语言处理自然语言处理.
- 医疗信息学 医疗信息学
- 医疗保健中的人工智能
背景情况:
- 电子健康记录包含大量的非结构化的临床笔记.
- 从这些笔记中提取标志和症状 (S&S) 对患者护理和研究至关重要.
- 大型语言模型 (LLM) 提供了自动化这种提取过程的潜力.
研究的目的:
- 评估GPT-4o在从临床笔记中提取S&S的性能.
- 评估GPT-4o对一般和尿路特定S&S的准确性.
- 测量GPT-4o的提取物的一致性.
主要方法:
- 利用 GPT-4o 来分析来自 MTSamples 库的临床笔记.
- 使用精度,回忆和F1得分对人类标记数据进行提取性能评估.
- 使用协议率和科恩的卡帕来衡量一致性.
主要成果:
- 对于整体的S&S提取,GPT-4o表现中等 (F1得分:68.1%).
- 尿路特异性S&S的表现显著改善,达到95.9%的F1得分.
- 观察到很高的一致性,尽管在协议指标中注意到了一些变化.
结论:
- GPT-4o有效地从临床笔记中提取尿路症状.
- 特定领域的微调可以进一步优化临床NLP任务的LLM性能.
- 在医疗保健中,LLM显示了增强非结构化数据分析的潜力.
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