多ADE:用于提取不良药物事件的多域基准
Xiang Dai1, Sarvnaz Karimi1, Abeed Sarker2
1CSIRO Data61, Sydney, Australia.
Journal of biomedical informatics
|November 13, 2024
概括
目前的模型在不同类型的文本中对药物不良事件提取的域概括性进行斗争. 需要进一步研究域调整,以便在药物监督中有效地部署现实世界.
科学领域:
- 自然语言处理自然语言处理.
- 药物监督 药物监督 药物监督
- 机器学习 机器学习
背景情况:
- 积极的不良事件监测依赖于从各种数据源中提取不良药物事件 (ADEs).
- 现有的数据集和任务主要集中在单个文本类型上,限制了模型的概括性.
- 跨多种文本 (例如,科学文献,社交媒体) 提取ADE的域泛化仍未得到充分探索.
研究的目的:
- 评估机器学习模型在不同文本域中提取ADE的当前能力.
- 开发一个全面的基准来评估ADE提取中的域泛化.
- 调查一个单一的ADE提取模型在各种文本类型中有效的可行性.
主要方法:
- 构建MultiADE基准,结合现有数据集和新的CADECv2数据集.
- CADECv2扩展了CADEC,为涵盖更广泛药物的在线帖子提供注释.
- 人类注释者根据详细指南对新数据集进行注释.
主要成果:
- 训练有素的模型在不同的文本域中表现出不完美的概括,阻碍了部署.
- 中间转移学习有希望,但需要进一步研究领域适应方法.
- 选择有用的培训实例的成本效益方法对于改善领域适应至关重要.
结论:
- 目前的ADE提取模型还不适合处理各种文本类型,而无需针对特定领域的调整.
- 域调整技术,特别是有效的实例选择,对于提高模型性能至关重要.
- MultiADE基准和CADECv2数据集是公开可用的,以推进药监信息提取的研究.
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