评估知识融合模型,以检测文本中的不良药物事件
Philipp Wegner1,2, Holger Fröhlich1,3, Sumit Madan1
1Department of Bioinformatics, Fraunhofer Institute for Algorithms and Scientific Computing (SCAI), Sankt Augustin, Germany.
PLOS digital health
|March 18, 2025
概括
知识融合与变压器模型有效地检测来自社交媒体和医学文本的不良药物事件 (ADEs). 这些先进的方法通过识别潜在的ADE提及,显著改善药物安全监测.
科学领域:
- 药监和自然语言处理 (NLP)
背景情况:
- 检测药物不良事件 (ADEs) 对于监管机构和制药行业持续监测药物安全至关重要.
- 社交媒体平台提供了丰富的现实世界患者报告的不良事件数据来源.
- 传统的方法需要改进,以有效地处理ADE检测的各种文本来源.
研究的目的:
- 评估知识融合方法与基于变压器的NLP模型相结合,用于提取ADE提及.
- 评估这些方法在各种数据集中的性能,包括社交媒体,患者网站和药物标签.
- 引入和测试一个集成变压器模型和图形注意力网络 (GAT) 的多模式架构.
主要方法:
- 制定了作为命名实体识别 (NER) 任务的ADE提取.
- 应用融合学习,以从本体学和知识图表获得的上下文知识来增强变压器模型.
- 开发了一个多模式架构,将变压器模型 (例如,ERNIE,BERT) 与GAT结合起来.
主要成果:
- 知识融合模型在多个实体 (PsyTAR,ADE,TAC) 中始终优于基线BERT模型.
- 一个多模式模型 (ERNIE + 知识) 在CADEC库中获得了71.84%的F1得分.
- 一种GAT和BERT组合实现了高F1得分,包括TAC库中的94.15%.
结论:
- 语境知识显著提高了用于ADE检测的知识融合模型的性能.
- 拟议的基于变压器和多模式的方法显示了改善药物监测的巨大潜力.
- 这些方法为挖掘真实世界的数据提供了一个强大的框架,以确保药物安全.
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