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挖掘社交媒体数据,研究流感疫苗的有效性,使用大型语言模型和思维链促进

Dongfang Xu¹, Guillermo López García¹, Karen O'Connor²

¹Cedars-Sinai Medical Center, Los Angeles, CA.

medRxiv : the preprint server for health sciences

|April 8, 2025

概括

大型语言模型 (LLM) 现在可以使用社交媒体数据实时估计流感疫苗 (VE) 的有效性. 这种新的方法为传统的公共卫生监测方法提供了更快,更具代表性的替代方案.

科学领域:

公共卫生公共卫生
计算流行病学计算流行病学
人工智能在医学中的应用

背景情况:

估计流感疫苗有效性 (VE) 对公共卫生政策至关重要.
目前的 VE 估计方法面临选择偏差和延迟报告等局限性.

研究的目的:

探索使用大型语言模型 (LLM) 进行实时流感VE估计,采用几次射击的思维链 (CoT) 提示.
挖掘社交媒体数据,以改善流感监测.

主要方法:

标注了2020-2021流感季的4000多条推文,以了解疫苗接种情况和测试结果.
开发并测试基于LLM的CoT提示数据提取策略.
将LLM的性能与传统的监督微调方法进行比较.

主要成果:

在推特注释过程中实现了高层次的注释者间协议.
最好的LLM提示策略在识别疫苗接种状态和测试结果方面达到F1得分超过87%.
这种LLM方法显著超过了传统的监督微调方法.

结论:

基于LLM的提示对于从社交媒体中提取相关的流感信息是有效的.
这种方法提供了一个有价值的实时监测工具,补充了传统的流行病学研究.
通过快速 VE 估计,LLM 为加强公共卫生决策提供了一个有希望的途径.

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