使用大型语言模型来检测和理解基于Web的论坛中的药物戒断事件:开发和验证研究
William Trevena1, Xiang Zhong1, Michelle Alvarado1
1Department of Industrial and Systems Engineering, The University of Florida, GAINESVILLE, FL, United States.
Journal of medical Internet research
|January 30, 2025
概括
像GPT-4o和BART这样的大型语言模型可以有效地从在线健康论坛中检测药物停用事件 (DDEs) 和其原因. 这项研究引入了一个框架和开放访问数据集,用于在数据稀疏的临床研究中研究DDEs.
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
- 计算语言学 计算语言学
背景情况:
- 像BART和GPT-4这样的大型语言模型 (LLM) 正在改变包括医疗保健应用在内的非结构化文本分析.
- 分析社交媒体数据提供了公共卫生见解,但检测药物停用事件 (DDEs) 仍然是一个挑战.
- 识别DDEs对于了解药物坚持和患者的结果至关重要.
研究的目的:
- 为在数据稀疏环境中开展临床研究制定灵活的框架.
- 在MedHelp网络论坛上使用LLMs识别DDEs及其根本原因.
- 发布第一个开源的DDE数据集,以促进未来的研究.
主要方法:
- 在MedHelp用户评论中使用LLM (GPT-4 Turbo,GPT-4o,DeBERTa,BART) 进行DDE检测和根源原因分析.
- 在没有任务特定培训的情况下,用于模型预测的零射击分类.
- 将用户的评论分类成句子,并应用各种策略来评估模型性能.
主要成果:
- 在确定DDE根源原因方面,GPT-4o实现了最高的准确性,预测错误率为12.9% (击损失).
- 在开源模型中,BART在DDE检测方面表现出色,在没有微调的情况下,F1得分为0.86.
- 该数据集包含10.7%的DDEs,表明在不平衡的数据中模型的稳定性.
结论:
- 开源和封闭源LLM (GPT-4o,BART) 通过从公共数据中进行零射击分类,有效地检测DDE和根源原因.
- 拟议的框架是强大且可扩展的,用于解决数据稀疏的临床研究问题.
- 预计开放访问的DDE数据集的发布将推动进一步的药监研究和发现.
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