Towards the development of a conceptual distance metric for the UMLS
Towards the development of a conceptual distance metric for the UMLS
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DOI：
10.1016/j.jbi.2004.02.001
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发表时间：
2004-04-01
影响因子：
4.5
通讯作者：
Cimino, JJ
中科院分区：
文献类型：
--
作者：
Caviedes, JE;Cimino, JJ
本研究的目的是探讨在统一医学语言系统（UMLS）框架下概念相似性度量的可行性。我们已经研究了一种基于概念之间父链接的最小数量的方法，并评估了其相对于人类专家对UMLS中三个术语的三组概念的估计的性能（即，MeSH、ICD9CM和SNOMED）。由此产生的定量度量使基于计算机的应用程序，使用决策阈值和近似匹配标准。所提出的概念匹配支持基于现成数据（通常表示为低级特定概念）的问题解决和推理（使用高级通用概念）。通过识别语义相似的概念，概念匹配也可以在没有精确甚至近似的词汇匹配的情况下进行推理。最后，概念匹配与术语开发和维护、机器学习研究、决策支持系统开发以及生物医学信息学和其他领域的数据挖掘研究有关。(C)2004年爱思唯尔公司All rights reserved.
The objective of this work is to investigate the feasibility of conceptual similarity metrics in the framework of the Unified Medical Language System (UMLS). We have investigated an approach based on the minimum number of parent links between concepts, and evaluated its performance relative to human expert estimates on three sets of concepts for three terminologies within the UMLS (i.e., MeSH, ICD9CM, and SNOMED). The resulting quantitative metric enables computer-based applications that use decision thresholds and approximate matching criteria. The proposed conceptual matching supports problem solving and inferencing (using high-level, generic concepts) based on readily available data (typically represented as low-level, specific concepts). Through the identification of semantically similar concepts, conceptual matching also enables reasoning in the absence of exact, or even approximate, lexical matching. Finally, conceptual matching is relevant for terminology development and maintenance, machine learning research, decision support system development, and data mining research in biomedical informatics and other fields. (C) 2004 Elsevier Inc. All rights reserved.