基于BERT的语言模型,用于从社交媒体中准确地提取药物不良事件:实施,评估和对药物监督实践的贡献
Fan Dong1, Wenjing Guo1, Jie Liu1
1National Center for Toxicological Research, US Food and Drug Administration, Jefferson, AR, United States.
Frontiers in public health
|May 8, 2024
概括
我们开发了一个基于变压器的双向编码器表示 (BERT) 模型,从社交媒体数据中提取药物不良事件. 该模型实现了高准确性,通过改善药物安全监督,推进了药监督.
科学领域:
- 药监和自然语言处理 (NLP)
- 计算语言学 计算语言学
- 医疗信息学 医疗信息学
背景情况:
- 社交媒体是药物安全监测和不良事件监测的丰富来源.
- 从社交媒体中提取药物不良事件给NLP和药物监督带来了挑战.
- 现有的研究缺乏对基于变压器的双向编码器表示 (BERT) 模型的详细实施和评估.
研究的目的:
- 开发和评估基于BERT的语言模型,以准确地从社交媒体中提取药物不良事件.
- 通过使用社交媒体数据,解决药监监中强有力的方法的需求.
- 提高识别药物不良事件的效率和准确性.
主要方法:
- 使用ADE-Corpus-V2数据集开发了一个基于BERT的语言模型.
- 优化了超参数,包括训练时代,批量大小和学习率.
- 对ADE-Corpus-V2和外部社交媒体数据集进行了十次保留评估.
主要成果:
- 在保留评估中获得高F1分 (高达0.9813).
- 对SMM4H数据的外部验证证实了模型的有效性,F1得分高达0.8127.
- 在检测药物不良事件方面表现出一致的高准确性.
结论:
- 基于BERT的模型对于在社交媒体数据中准确识别药物不良事件是有效的.
- 该研究提供了一个全面的设计和对NLP在药监中的评估.
- 通过新兴数据源,为推进药监督实践做出贡献.
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