增强COVID-19疫苗注射-不良事件关联与大型语言模型的关系提取
Yiming Li1, Deepthi Viswaroopan1, William He2
1The University of Texas Health Science Center at Houston.
Research square
|April 1, 2025
概括
研究人员使用人工智能模型分析在VAERS和社交媒体上报道的COVID-19疫苗不良事件. GPT-3.5在识别疫苗注射和不良事件之间的关系方面表现出高准确性,改善了安全监测.
科学领域:
- 生物医学信息学 生物医学信息学
- 自然语言处理自然语言处理.
- 疫苗安全监督 疫苗安全监督
背景情况:
- COVID-19病毒的演变需要持续的疫苗开发和监测.
- 疫苗不良事件 (AE) 需要仔细监测,以确保公共安全.
- 了解特定的COVID-19疫苗注射和AE之间的联系至关重要.
研究的目的:
- 提取和分析COVID-19疫苗注射和不良事件之间的关系.
- 为了比较先进的人工智能模型 (GPT-3.5,GPT-4) 与关系提取的传统模型 (RNNs,BioBERT) 的性能.
- 利用来自疫苗不良事件报告系统 (VAERS) 和社交媒体的数据.
主要方法:
- 利用VAERS,Twitter和Reddit的数据,包括771个关系对.
- 使用最先进的GPT模型 (GPT-3.5,GPT-4) 和传统模型 (RNNs,BioBERT).
- 应用后处理规则和使用精度,回忆和F1分数评估性能.
主要成果:
- 初级COVID-19疫苗系列的常见副作用包括手臂疼痛,疲劳和头痛.
- 增剂疫苗的副作用呈现出更广泛的不良事件.
- 微调的GPT-3.5在关系提取方面实现了0.94的精度,1的回忆率和0.97的F1得分.
结论:
- 大型语言模型和深度学习有效地从各种数据源中提取疫苗-AE关系.
- 这些发现有助于加强疫苗安全监测和对疫苗接种后症状的临床理解.
- 这项研究为NLP和生物医学信息学在药监中的应用创造了先例.
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