对自然语言机器学习模型的验证,用于安全文献监督
Jiyoon Park1, Malek Djelassi2, Daniel Chima1
1Global Patient Safety, Chief Medical Office, AstraZeneca, Gaithersburg, MD, USA.
Drug safety
|November 8, 2023
概括
一种用于自动化文献监测的新型深度学习模型显示了与人类团队相似的召回率,使其有资格用于药监过程. 这种方法可以提高安全信号的检测,同时管理与人工智能相关的药物安全风险.
科学领域:
- 药物监督 药物监督 药物监督
- 人工智能在药物安全方面的作用
- 自然语言处理自然语言处理.
背景情况:
- 对药物安全监测的手册文献审查是耗时的.
- 由于可用的数据和NLP算法性能,自动化是可行的.
- 深度学习模型在药物监督中提出了独特的验证挑战.
结论:
- 未来,现实世界的性能特征对于合格的AI模型在监视中至关重要.
- 仔细检查模型的一致性和故障模式可确保安全实施.
- 未来的改进和社区合作可以进一步推进自动化文献监控.
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