通过使用大型语言模型,推进患者偏好定性评估的科学
Ted Grover1, Emanuel Krebs1, Deirdre Weymann1,2
1Regulatory Science Lab, BC Cancer Research Institute, Vancouver, Canada.
PLOS digital health
|March 12, 2026
概括
大型语言模型 (LLM) 可以对患者偏好数据进行感应主题分析. 虽然有前途,但人类监督对于细微的定性分析至关重要.
科学领域:
- 医疗保健研究 医疗保健研究
- 人工智能在医学中的应用
- 定性分析 定性分析
背景情况:
- 患者体验对于以患者为中心的医疗保健至关重要.
- 大型语言模型 (LLM) 在医疗保健中越来越多地使用,但不是用于定性患者偏好评估.
- 对患者采访/焦点小组的专题分析是常见的,但劳动密集型.
研究的目的:
- 调查LLMs对定性患者偏好数据的感应主题分析的有用性.
- 评估与人类分析的主题相比,LLM生成的主题的语义相似性和风格特征.
- 为此任务评估不同LLM提示框架的性能.
主要方法:
- 使用Hermes-3-Llama-3.1-70B LLM对现有的焦点小组成绩单进行主题分析.
- 利用了三个优化的提示框架,其中一个侧重于主题和.
- 使用 Sentence-T5-XXL 嵌入和 Jaccard 相似系数评估语义相似性.
- 对LLM生成的主题评估了词汇多样性和阅读等级级度量.
主要成果:
- 所有测试的LLM提示框架都实现了与人类分析的主题的中度语义重叠 (贾卡德系数0.46-0.64).
- 最好表现的框架,专注于主题和,显示语义重叠比基准高12%,并与阅读等级级度量保持一致.
- 最不有效的框架产生了适度的重叠,但也产生了人类分析中不存在的幻觉发现.
结论:
- 在定性患者偏好数据上,LLM可以有效地进行感应性主题分析,产生与人类分析内容和风格相似的主题.
- 特定领域的背景对于增强LLM来实现准确的主题分析至关重要.
- 法律法学显示出增强定性分析的潜力,但人类专业知识对于解释上下文细微差别至关重要.
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