临床和临床前术语之间的映射:eTRANSAFE的罗塞塔石方法
Erik M van Mulligen1, Rowan Parry2, Johan van der Lei2
1Dept of Medical Informatics, Erasmus University Medical Center, Rotterdam, The Netherlands. e.vanmulligen@erasmusmc.nl.
Journal of biomedical semantics
|August 22, 2025
概括
采用SNOMED CT,Rosetta Stone方法有效地绘制出临床前和临床数据,达到95%的精度. 这种方法通过将不同的术语与不同的精确度联系起来来增强翻译研究.
科学领域:
- 生物医学信息学
- 翻译研究
- 数据整合
背景情况:
- 翻译性研究需要整合临床前和临床数据,而这些数据往往受到不同的术语和细节的阻碍.
- 为了应对这一整合挑战,eTRANSAFE项目旨在开发工具.
- 现有的数据格式包括单个预先协调的临床概念和复杂的临床前概念组合.
研究的目的:
- 开发和评估用于将临床前概念映射到临床概念的罗塞塔石方法.
- 为了实现不同数据类型的不同精度的灵活映射.
- 促进研究中各种临床前和临床数据的结合.
主要方法:
- 从临床前 (组织病理学,SEND,大鼠解剖学) 和临床 (MedDRA) 术语的概念被映射到SNOMED CT作为中间体.
- 手动创建从临床前到SNOMEDCT的映射,利用现有的临床到SNOMEDCT映射.
- 制定一个协调模板来定义映射关系,并分配不准确性惩罚分数.
- 使用60个概念的子集对词汇术语匹配进行语义映射的评估.
主要成果:
- 超过34,000个概念被映射到SNOMED CT中.
- 一个术语服务被创建为动态提供准确和不准确的映射.
- 罗塞塔石的方法在映射中达到95%的精度,显著超过了词汇匹配 (22%).
结论:
- 罗塞塔石的方法成功地利用SNOMEDCT来弥合临床前和临床数据.
- 能够生成精确和不精确的映射增加了可连接数据的数量.
- 这种语义映射策略对于推进翻译研究用例具有价值.
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