基于本体学的语义相似度测量用于对药物安全中的医学概念进行集群
Jeffery L Painter1, François Haguinet2, Gregory E Powell1
1GSK, Durham, NC, USA.
AMIA ... Annual Symposium proceedings. AMIA Symposium
|February 23, 2026
概括
基于内在信息内容的语义相似性测量 (SSM) 对药监有希望. 这些方法改善了MedDRA首选术语 (PT) 的聚类,有助于早期检测药物安全信号.
科学领域:
- 药监和生物医学信息学
- 计算语言学和本体学工程 计算机语言学和本体学工程
背景情况:
- 语义相似度 (SSM) 在生物医学研究中至关重要,但在药监中未得到充分利用.
- 集群MedDRA首选术语 (PTs) 对于分析药物安全数据至关重要.
- 现有的方法需要评估它们在药监环境中的有效性.
研究的目的:
- 评估六个基于本体学的SSM,以评估它们在集群MedDRA PTs中的有效性.
- 通过使用统一医疗语言系统 (UMLS) 评估不同SSM方法的性能.
- 识别可增强药监工作流程的SSM,用于早期信号检测.
主要方法:
- 六个基于本体学的SSM被评估用于集群MedDRA PTs.
- 统一的医学语言系统 (UMLS) 用于相似性计算.
- 开发了一个具有Java API和Python/R接口的高吞吐量框架,用于大规模分析.
- 集群精度是使用F1评分测量的,并根据专家审查和标准MedDRA查询 (SMQ) 进行验证.
主要成果:
- 基于路径的SSM (WUPALMER,LCH) 显示出适度的性能,F1分数分别为0.36和0.28.
- 基于内在信息内容 (IC) 的SSM,特别是INTRINSIC_LIN和SOKAL,实现了卓越的集群精度,F1得分为0.403.
- 基于IC的方法显示,与基于路径的方法相比,在医学上有意义的中心体周围更好地分组PT.
结论:
- 基于内在信息内容的SSM对改善药物监督非常有希望.
- 这些方法可以通过改进PT聚类来提高药物安全性数据分析的效率.
- 基于IC的SSM的应用可以导致更早地检测不良药物事件信号,并减少手动审查工作.
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