Towards quality improvement of vaccine concept mappings in the OMOP vocabulary with a semi-automated method.
Towards quality improvement of vaccine concept mappings in the OMOP vocabulary with a semi-automated method.
复制标题
通过半自动化的方法，在OMOP词汇量中质量改进疫苗概念映射。
DOI：
10.1016/j.jbi.2022.104162
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发表时间：
2022-10
影响因子：
4.5
通讯作者：
Cui, Licong
中科院分区：
文献类型：
--
作者：
Abeysinghe, Rashmie;Black, Adam;Kaduk, Denys;Li, Yupeng;Reich, Christian;Davydov, Alexander;Yao, Lixia;Cui, Licong
观察性医学成果伙伴关系（OMOP）通用数据模型（CDM）提供了一个统一的模型来集成不同的真实世界数据（RWD）源。OMOP CDM的一个组成部分是标准化词汇表（以下简称为OMOP词汇表），它能够组织和标准化OMOP CDM各个临床领域的医学概念。对于来自不同源词汇表的具有相同含义的概念，将一个概念指定为标准概念，而将其他概念指定为非标准概念或源概念并映射到标准概念。然而，由于源词汇的异质性，可能存在映射问题，如错误的映射和丢失的映射在OMOP词汇，这可能会影响与RWD的下游分析的结果。在本文中，我们专注于OMOP词汇表中疫苗概念映射的质量保证，这是准确利用RWD对疫苗的影响所必需的。我们介绍了一个半自动化的词汇方法来审计疫苗映射的OMOP词汇。我们生成了两种类型的疫苗对：映射的和未映射的，其中映射的疫苗对是具有“映射到”关系的疫苗概念对，而未映射的疫苗对是没有“映射到”关系的疫苗概念对。我们将每个疫苗概念名称表示为一组单词，并导出术语差异对（即，名称差异）。如果映射和未映射的疫苗对可以获得相同的项差对，则这被认为是潜在的映射不一致。将这种方法应用于OMOP中的疫苗映射，共获得2087个潜在的映射不一致。随机选择的200个样本由领域专家进行评估，以识别，验证和分类的不一致性。专家们确定了95个案例，揭示了有效的绘图问题。其余105个案例被发现无效，因为映射中使用的外部和/或上下文信息没有反映在疫苗的概念名称中。这表明，我们的半自动化的方法显示出的承诺，在识别映射不一致的疫苗概念之间的OMOP词汇。
The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) provides a unified model to integrate disparate real-world data (RWD) sources. An integral part of the OMOP CDM is the Standardized Vocabularies (henceforth referred to as the OMOP vocabulary), which enables organization and standardization of medical concepts across various clinical domains of the OMOP CDM. For concepts with the same meaning from different source vocabularies, one is designated as the standard concept, while the others are specified as non-standard or source concepts and mapped to the standard one. However, due to the heterogeneity of source vocabularies, there may exist mapping issues such as erroneous mappings and missing mappings in the OMOP vocabulary, which could affect the results of downstream analyses with RWD. In this paper, we focus on quality assurance of vaccine concept mappings in the OMOP vocabulary, which is necessary to accurately harness the power of RWD on vaccines. We introduce a semi-automated lexical approach to audit vaccine mappings in the OMOP vocabulary. We generated two types of vaccine-pairs: mapped and unmapped, where mapped vaccine-pairs are pairs of vaccine concepts with a “Maps to” relationship, while unmapped vaccine-pairs are those without a “Maps to” relationship. We represented each vaccine concept name as a set of words, and derived term-difference pairs (i.e., name differences) for mapped and unmapped vaccine-pairs. If the same term-difference pair can be obtained by both mapped and unmapped vaccine-pairs, then this is considered as a potential mapping inconsistency. Applying this approach to the vaccine mappings in OMOP, a total of 2087 potentially mapping inconsistencies were obtained. A randomly selected 200 samples were evaluated by domain experts to identify, validate, and categorize the inconsistencies. Experts identified 95 cases revealing valid mapping issues. The remaining 105 cases were found to be invalid due to the external and/or contextual information used in the mappings that were not reflected in the concept names of vaccines. This indicates that our semi-automated approach shows promise in identifying mapping inconsistencies among vaccine concepts in the OMOP vocabulary.
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