人工智能驱动的药物监测:对基于临床文本的不良药物事件检测中机器和深度学习的审查,用于基准数据集的基准数据集
Yiming Li1, Wei Tao2, Zehan Li1
1McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX 77030, USA.
Journal of biomedical informatics
|March 6, 2024
概括
深度学习模型擅长通过命名实体识别提取不良药物事件 (ADEs),而机器学习模型在关系分类方面表现良好. 来自变压器的双向编码器表示 (BERT) 在端到端ADE提取中表现出最佳性能.
科学领域:
- 药物监督 药物监督 药物监督
- 计算语言学 计算语言学
- 生物医学信息学 生物医学信息学
背景情况:
- 药物不良事件 (ADEs) 对患者安全构成重大风险.
- 从各种数据源中自动提取ADEs对于有效的药物监督至关重要.
- 机器学习和深度学习为增强ADE检测提供了有前途的途径.
研究的目的:
- 评估机器学习 (ML) 和深度学习 (DL) 对药物不良事件 (ADE) 提取的有效性.
- 在命名实体识别 (NER) 和ADE的关系分类 (RC) 中比较ML和DL技术.
- 分析特征影响并探索跨不同数据类型的ADE提取.
主要方法:
- 使用有针对性的搜索术语对PubMed进行了广泛的文献审查.
- 采用雪球式的方法来确定其他相关研究.
- 对12篇精选的文章进行分析,重点关注用于ADE提取任务的ML/DL.
主要成果:
- 深度学习模型在命名实体识别 (NER) 中超过了ML模型.
- 梯度提升,多层感知器和随机森林在关系分类 (RC) 中表现出色.
- 来自变压器的双向编码器表示 (BERT) 在端到端ADE提取中表现出卓越的性能.
结论:
- 深度学习,特别是BERT,显示了ADE提取的巨大潜力.
- 未来的研究应该专注于提高关键ADE组件的NER精度.
- 增强的ADE提取可以显著改善药物安全监测和患者的治疗结果.
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