从自由文本中提取Covid-19传播的情况,使用大型语言模型
Gaston Bizel-Bizellot1, Simon Galmiche2,3, Benoît Lelandais1,4
1Institut Pasteur, Université Paris Cité, Imaging and Modeling Unit, Paris, France.
Nature communications
|July 2, 2025
概括
大型语言模型 (LLM) 可以自动从在线调查中提取COVID-19传播情况. 这种方法增强了流行病学的洞察力,并可能减少对未来流行病反应的传统封闭式问题的依赖.
科学领域:
- 流行病学 流行病学
- 计算语言学 计算语言学
- 公共卫生 公共卫生
背景情况:
- 快速识别传染病传播对于有效的缓解至关重要.
- 传统的流行病学调查通常依赖于封闭式问题,可能限制数据丰富性.
研究的目的:
- 探索大型语言模型 (LLM) 在自动从自由文本调查答复中提取COVID-19传播情况的实用性.
- 评估基于LLM的分类和主题建模的性能,以识别感染环境.
主要方法:
- 法国一项全国性的在线研究招募了545958名最近感染SARS-CoV-2的成年人.
- 使用预训练的LLM进行了分类模型的训练,以从开放式问题中预测预定义的感染背景.
- 使用主题建模来识别不依据预定义的类别的传输集群.
主要成果:
- 该LLM分类模型达到75%的准确性,当排除高反应时增加到91%.
- 主题建模确定了23个集群,与预定义的背景保持一致,并揭示了更细致的传输细节.
- 自由文本的LLM分析显示了在调查中补充或取代闭式问题的潜力.
结论:
- 基于自由文本流行病学数据的LLM分析可以加速和丰富获得公共卫生见解.
- 这种方法为了解疾病传播动态提供了一个有希望的工具,特别是在新出现的传染病和未来的流行病的背景下.
- 自动提取传播情况可以提高流行病监测的效率和深度.
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