Pan Yuanyi, Manuel Warren, Abeysinghe Rashmie, Zheng Jie, Davydov Alexander, Yang Qi, Lin Asiyah Yu, Cui Licong, He Yongqun Oliver
University of Michigan Medical School, Ann Arbor, MI, USA.
McWilliams School of Biomedical Informatics, The University of Texas Health Science Center at Houston, Houston, TX, USA.
bioRxiv. 2025 Jul 18:2025.07.15.664501. doi: 10.1101/2025.07.15.664501.
With many vaccines developed and used, it is critical to standardize vaccine information. The OHDSI OMOP Common Data Model (CDM), widely used to support EHR data integration and analysis, leverages CVX, RxNorm, and RxNorm Extension codes to standardize vaccine-related records. However, these terminologies lack robust semantic relations, making the vaccine classification ineffective in OMOP CDM. To address this issue, our OHDSI Vaccine Vocabulary Working Group proposes to use the Vaccine Ontology (VO) to map these standards and build up its own semantic relations. As a first study of the work, we performed the mapping and alignment of the Vaccine Administered (CVX) codes with the VO using a combination of semi-automatic and manual mapping methods.
A total of 273 CVX terms were first collected and classified. A high-level VO design pattern and an exact one-to-one mapping strategy were developed to guide the CVX-to-VO term mapping. To facilitate the manual mapping and harmonization process, we also developed and evaluated three semi-automated mapping approaches utilizing lexical and semantic information of vaccine concepts to map CVX to VO. These approaches suggested candidate VO mappings for CVX terms and also indicated CVX terms that were unmappable to VO and required new term additions to VO. The application of the best approach to the 2022-10-05 release of VO achieved an accuracy of 85.55% for its suggestions. The suggestions made by the semi-automated approaches were taken into account to further enhance the mappings, which led to our eventual mapping of all CVX terms to the latest version of VO. We innovatively proposed the inclusion of the 'passive vaccine' branch in VO, which includes 24 immunoglobulins and antitoxins from CVX as passive vaccines. A specific CVX-VO OWL file was developed and added to the VO GitHub. Use case queries were developed to demonstrate its support for computer-assisted queries of vaccine groups based on CVX-VO hierarchies.
All CVX terms were mapped to the VO using our combined semi-automatic and manual mapping methods. The mapped results enhanced semantic vaccine classification, providing a basis for further OMOP vaccine classification and EHR data analysis.
随着众多疫苗的研发和使用，规范疫苗信息至关重要。OHDSI OMOP通用数据模型（CDM）被广泛用于支持电子健康记录（EHR）数据的整合与分析，它利用CVX、RxNorm和RxNorm扩展代码来规范与疫苗相关的记录。然而，这些术语缺乏强大的语义关系，使得在OMOP CDM中疫苗分类效果不佳。为解决这一问题，我们的OHDSI疫苗词汇工作组提议使用疫苗本体（VO）来映射这些标准并建立其自身的语义关系。作为这项工作的首次研究，我们使用半自动和手动映射方法相结合的方式，对已接种疫苗（CVX）代码与VO进行了映射和比对。
首先共收集并分类了273个CVX术语。开发了一种高级VO设计模式和一种精确的一对一映射策略，以指导CVX到VO术语的映射。为便于手动映射和协调过程，我们还开发并评估了三种利用疫苗概念的词汇和语义信息将CVX映射到VO的半自动映射方法。这些方法为CVX术语提出了候选VO映射，还指出了无法映射到VO且需要在VO中添加新术语的CVX术语。将最佳方法应用于2022年10月5日发布的VO时，其建议的准确率达到了85.55%。考虑了半自动方法提出的建议以进一步完善映射，最终我们将所有CVX术语映射到了VO的最新版本。我们创新性地提议在VO中纳入“被动疫苗”分支，其中包括来自CVX的24种免疫球蛋白和抗毒素作为被动疫苗。开发了一个特定的CVX-VO OWL文件并添加到VO GitHub中。开发了用例查询以展示其对基于CVX-VO层次结构的疫苗组计算机辅助查询的支持。
使用我们的半自动和手动映射方法相结合，将所有CVX术语映射到了VO。映射结果增强了疫苗语义分类，为进一步的OMOP疫苗分类和EHR数据分析提供了基础。
