用于检测药物不良事件的自然语言处理:系统性审查协议
Imane Guellil1, Jinge Wu2, Aryo Pradipta Gema1
1The University of Edinburgh, Edinburgh, Scotland, UK.
NIHR open research
|February 11, 2025
概括
本系统性审查综合了自然语言处理 (NLP) 方法来检测药物不良事件 (ADEs). 这些发现将有助于研究人员改善ADE预测和患者的治疗结果.
科学领域:
- 药监和药物安全 药监和药物安全
- 医疗信息学 医疗信息学
- 计算语言学 计算语言学
背景情况:
- 检测药物不良事件 (ADEs) 是医疗保健研究的一个关键领域.
- 使用自然语言处理 (NLP) 来自动提取ADEs为知识蒸和风险预测提供了重大潜力.
- 改善对ADE倾向性因素的管理可以带来更好的患者结果.
研究的目的:
- 从各种自由文本来源系统地审查和整合自然语言处理 (NLP) 方法的证据,以自动检测ADE.
- 提高对当前环境的理解和NLP工具在提取ADE信息中的有效性.
- 为开发先进的NLP方法提供信息,并支持医疗保健中的临床研究人员.
主要方法:
- 进行了系统性审查,搜索了六个主要的学术数据库 (Embase,Medline,科学网,ACM,IEEE,Scopus).
- 研究经历了严格的标题,摘要和全文选.
- 进行了数据提取,表格化和研究特征和发现的总结,并使用PROBAST工具评估偏差风险.
主要成果:
- 截至2024年12月,共有178项相关研究被确定并纳入审查中.
- 从所有包含的论文中提取数据已经完成.
- 系统性审查论文目前处于合成和编写阶段,自2022年8月以来,正在不断完善资格标准和数据提取.
结论:
- 大多数审查的作品集中在从文本中检测和提取ADEs上.
- 很大一部分研究还涉及用于ADE识别的二进制文本分类.
- 确定的挑战包括不平衡的数据集,处理缩写,以及为罕见的ADEs实现高精度.
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