应用大型语言模型来解释医疗保健中的定性访谈
Marie Wosny1,2, Janna Hastings1,2,3
1School of Medicine, University of St. Gallen (HSG), Switzerland.
Studies in health technology and informatics
|August 23, 2024
概括
大型语言模型 (LLM) 显示出分析定性医疗数据的前景,加快解释速度,同时保留丰富的见解. 这种方法可以帮助将患者和专业的观点整合到可持续的医疗保健进步中.
科学领域:
- 医疗保健系统 医疗保健系统
- 定性数据分析 数据分析.
- 人工智能的人工智能
背景情况:
- 医疗保健的挑战需要整合患者和专业人士的观点.
- 目前的定性数据分析是耗时和劳动密集的.
- 现有的自动化方法,如主题建模,可能会减少数据的丰富性.
研究的目的:
- 评估大型语言模型 (LLM) 用于对定性面试数据的半自动解释.
- 将基于LLM的方法与传统的主题建模和手动分析进行比较.
- 探索LLMs在提高医疗保健的定性研究效率和深度方面的潜力.
主要方法:
- 开发了一种利用LLMs进行定性数据解释的新方法.
- 对LLM的表现与主题建模技术进行了比较.
- 该研究使用了两个不同的定性访谈数据集进行验证.
主要成果:
- 劳动法学证明了在支持对定性访谈数据的解释方面具有潜力.
- 与主题建模相比,LLM方法在保留数据丰富方面表现出有希望.
- 探索性发现表明,LLM可以帮助纳入各种人类观点.
结论:
- 在医疗保健研究中,LLM为半自动定性数据分析提供了可行的工具.
- 这项技术可以加速翻译过程,使其更高效.
- 通过更好地整合利益相关者的见解,LLM有潜力为可持续发展的医疗保健系统的发展做出重大贡献.
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