从SNOMED CT本体学中获得临床术语嵌入
Fuad Abu Zahra1, Rohit J Kate1
1Department of Computer Science, University of Wisconsin-Milwaukee, Milwaukee, WI, USA.
Journal of biomedical informatics
|December 9, 2023
概括
本研究引入了一种使用SNOMED CT本体学创建临床术语嵌入的新方法,与传统方法相比,改进了术语相似性和规范化任务.
科学领域:
- 医疗信息学 医疗信息学
- 自然语言处理自然语言处理.
- 计算语言学 计算语言学
背景情况:
- 传统的临床术语嵌入缺乏本体学知识.
- 基于图形的方法捕捉了概念嵌入,但不是术语嵌入.
- 现有的方法与相似或相反的术语作斗争.
研究的目的:
- 从SNOMED CT本体学中开发一种用于生成临床术语和词嵌入的新方法.
- 改善临床术语相似性和规范化任务.
- 解决现有的基于语料库和基于本体学的嵌入方法的局限性.
主要方法:
- 利用基于图形的方法,从SNOMED CT.获得临床概念的嵌入.
- 训练了一个深度学习模型,将临床术语映射到概念嵌入.
- 引入了一个模式学习方法来区分相同/不同的概念,以改善规范化.
主要成果:
- 新型嵌入在预测五个数据集的临床术语相似性方面表现优于基于语料库的嵌入.
- 该方法在临床术语规范化方面取得了竞争力的准确性.
- 通过学习模式来补充正常化,表现得更好.
结论:
- 开发的方法有效地将本体学知识整合到临床术语嵌入中.
- 嵌入式增强了相似性和规范化任务的性能.
- 未来的工作包括为基于句子的任务结合基于本体的和基于语料的嵌入.
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