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相关实验视频

Updated: May 21, 2025

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness
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在孕产妇健康研究中进行主题分析的生成人工智能:使用大型语言模型编码半结构面试.

Shan Qiao1,2, Xingyu Fang3,4,5, Junbo Wang6

  • 1Department of Health Promotion, Education, and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USA.

Applied psychology. Health and well-being
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概括

生成型人工智能 (GenAI) 可以高效地编码定性面试数据,将编码时间减少81%,准确度超过80%. 需要进一步的研究来解决这种自动化主题分析方法的局限性.

关键词:
编码 编码 编码 编码生成性AI是一种人工智能.诱导编码是一种诱导编码.孕产妇健康 孕产妇健康专题分析 专题分析

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科学领域:

  • 定性研究方法 定性研究方法
  • 计算语言学 计算语言学
  • 医疗保健中的人工智能

背景情况:

  • 定性数据的主题分析依赖于编码半结构面试成绩单.
  • 手动编码是劳动密集型和耗时的,阻碍了有效的数据分析.
  • 生成型人工智能 (GenAI) 为提高定性编码效率提供了潜在的解决方案.

研究的目的:

  • 提出和评估一个使用GenAI的计算管道,从采访成绩单中自动提取主题.
  • 与手动方法相比,评估GenAI在感应编码中的效率和准确性.

主要方法:

  • 利用ChatGPT对产妇护理提供者的面试成绩单进行诱导编码.
  • 开发了结构化提示,以指导ChatGPT在没有预定义方案的情况下生成和总结代码.
  • 通过比较人工智能生成的代码与手工衍生代码来评估GenAI的性能.

主要成果:

  • 在检测和总结定性访谈数据中的代码方面,GenAI表现出显著的前景.
  • 在诱导编码任务中,ChatGPT的准确度超过了80%.
  • 基因人工智能将编码所需的时间减少了令人印象深刻的81%.

结论:

  • 基因人工智能模型可以有效地处理语言数据,用于定性研究中的语义识别.
  • 包括潜在的不准确性,偏见和隐私问题在内的挑战需要仔细考虑.
  • 未来的研究应该专注于改进GenAI模型以提高可靠性和解决定性应用的局限性.