用大型语言模型分析患者的观点:对情绪进行横截面研究,并根据知情同意的例外进行主题分类
Aaron E Kornblith1, Chandan Singh2,3, Johanna C Innes4
1University of California San Francisco, San Francisco, CA, USA. Aaron.Kornblith@ucsf.edu.
Scientific reports
|February 20, 2025
概括
大型语言模型 (LLM) 在信息化同意例外 (EFIC) 研究期间分析患者观点方面表现强,与人类审查员相似. 法律法规提高效率,但应该补充,而不是取代,在医疗保健文本分析的人类判断.
科学领域:
- 医疗保健中的人工智能
- 自然语言处理自然语言处理.
- 临床研究伦理学
背景情况:
- 大型语言模型 (LLM) 为医疗保健环境中高效的文本分析提供了潜力.
- 紧急研究中的知情同意 (EFIC) 程序的例外涉及放弃同意,需要仔细分析患者的观点.
- 与人类审稿人对LLM绩效的评估对于它们在敏感研究环境中的可靠应用至关重要.
研究的目的:
- 评估LLM在分析EFIC采访中的患者观点方面的能力.
- 为了比较LLM在情绪极性评估和主题分类方面的表现与人类评审者.
- 为了确定LLMs是否可以在EFIC研究中实现与人类判断相提并论的性能.
主要方法:
- 分析了102次EFIC社区采访 (每次46个问题),来自紧急医疗服务中儿童剂量优化发作研究.
- 评估5个LLM,包括GPT-4,用于情绪极性评分 (5分级) 和主题分类.
- 三名人类审稿人的并行分析,使用科恩的卡帕和分类准确度测量了协议.
主要成果:
- 在情绪极性评估方面,LLMs与人类评审者达成了实质性一致 (科恩的卡帕:0.69).
- 通过LLM,获得了高主题分类准确度 (0.868),相当于人类之间的分类协议 (0.867).
- 课程简化工具 (LLMs) 促进了大规模的分析,使得可以在不同网站,问题和主题类进行比较.
结论:
- 在EFIC采访中,LLM显示出有效分析患者观点的巨大潜力,在情绪和主题分析中匹配人类表现.
- 虽然LLM提高了效率并减少了主观性,但偶尔的表现不佳表明它们应该增强,而不是取代人类的判断力.
- 未来的研究应该专注于将LLM整合到EFIC流程中,以优化效率并确保准确的患者视角分析.
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