Abstract

Objective

The Unified Medical Language System (UMLS) integrates various source terminologies to support interoperability between biomedical information systems. In this article, we introduce a novel transformation-based auditing method that leverages the UMLS knowledge to systematically identify missing hierarchical IS-A relations in the source terminologies.

Materials and Methods

Given a concept name in the UMLS, we first identify its base and secondary noun chunks. For each identified noun chunk, we generate replacement candidates that are more general than the noun chunk. Then, we replace the noun chunks with their replacement candidates to generate new potential concept names that may serve as supertypes of the original concept. If a newly generated name is an existing concept name in the same source terminology with the original concept, then a potentially missing IS-A relation between the original and the new concept is identified.

Results

Applying our transformation-based method to English-language concept names in the UMLS (2019AB release), a total of 39 359 potentially missing IS-A relations were detected in 13 source terminologies. Domain experts evaluated a random sample of 200 potentially missing IS-A relations identified in the SNOMED CT (U.S. edition) and 100 in Gene Ontology. A total of 173 of 200 and 63 of 100 potentially missing IS-A relations were confirmed by domain experts, indicating that our method achieved a precision of 86.5% and 63% for the SNOMED CT and Gene Ontology, respectively.

Conclusions

Our results showed that our transformation-based method is effective in identifying missing IS-A relations in the UMLS source terminologies.

中文翻译：

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一种基于转换的方法，用于在统一医学语言系统中审核IS-A生物医学术语的层次结构。

摘要

目的

统一医学语言系统（UMLS）集成了各种源术语，以支持生物医学信息系统之间的互操作性。在本文中，我们介绍了一种新颖的基于转换的审核方法，该方法利用UMLS知识来系统地识别源术语中缺少的层次化IS-A关系。

材料和方法

给定UMLS中的概念名称，我们首先确定其基础和次要名词块。对于每个识别出的名词块，我们生成比名词块更通用的替换候选。然后，我们用名词替换候选替换名词块，以生成可能用作原始概念超类型的新潜在概念名称。如果新生成的名称是与原始概念相同的源术语中的现有概念名称，则将标识原始概念和新概念之间可能存在的IS-A关系。

结果

将我们基于转换的方法应用于UMLS（2019AB版本）中的英语概念名称，在13个源术语中总共检测到39 359个可能丢失的IS-A关系。领域专家评估了SNOMED CT（美国版）中确定的200个可能缺失的IS-A关系和Gene Ontology中的100个潜在抽样IS-A关系的随机样本。领域专家共确认了173个潜在的IS-A关系中的200个中的173个和100个潜在的IS-A关系中的63个，这表明我们的方法对SNOMED CT和基因本体论的准确度分别为86.5％和63％。

结论

我们的结果表明，基于转换的方法可有效地识别UMLS源术语中缺失的IS-A关系。