在韩国不良事件报告系统中,自动从药物不良事件叙述中提取全面的药物安全信息,使用自然语言处理技术
Siun Kim1, Taegwan Kang2,3, Tae Kyu Chung4
1Biomedical Research Institute, Seoul National University Hospital, Seoul, South Korea.
Drug safety
|June 17, 2023
概括
这项研究通过使用自然语言处理 (NLP) 从不良事件叙述中提取信息来提高药物安全数据的完整性. 开发的KAERS-BERT模型显著改善了自发报告系统 (SRS) 中的数据质量.
科学领域:
- 药监和药物安全 药监和药物安全
- 自然语言处理 (NLP) 是一种自然语言处理.
- 医疗信息学 医疗信息学
背景情况:
- 自发报告系统 (SRS) 对于药物监测至关重要,但在数据完整性方面面临挑战.
- 监管机构依赖于SRS数据,强调需要提高数据质量.
研究的目的:
- 定义NLP任务,从不良事件 (ADE) 叙述中提取全面的药物安全信息.
- 使用韩国不良事件报告系统 (KAERS) 开发这些NLP任务的基线模型.
主要方法:
- 根据ICH E2B (R3) 制定了基于ICH E2B (R3) 的注释指南,用于手动注释3723个ADE叙述.
- 使用120万个ADE叙述创建了一个特定域的韩国双向编码器从变压器表示 (KAERS-BERT) 模型.
- 定义了21个单词实体,6个实体标签和49个NLP提取任务的关系.
主要成果:
- 卡尔斯-伯特模型在命名实体识别 (NER) 方面获得了83.81%的F1分数,在句子提取方面达到76.62%.
- 使用NER模型在KAERS结构化字段中的数据完整性平均提高了3.24%.
- 在大多数定义的NLP任务中,KAERS-BERT模型的表现优于其他基线模型.
结论:
- 制定了从ADE叙述中提取全面的药物安全信息作为NLP任务.
- 开发了一个注释的语料库和强大的基线模型,以提高SRS数据库质量.
- 开发的NLP模型和语料库可以显著提高自发报告系统的数据质量.
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