An ontology-based approach for harmonization and cross-cohort query of Alzheimer's disease data resources.
An ontology-based approach for harmonization and cross-cohort query of Alzheimer's disease data resources.
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DOI：
10.1186/s12911-023-02250-z
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发表时间：
2023-08-04
影响因子：
3.5
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中科院分区：
文献类型：
--
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在美国，国家阿尔茨海默病协调中心（NACC）和阿尔茨海默病神经成像倡议（ADNI）是阿尔茨海默病（AD）研究的两大数据共享资源。NACC和ADNI努力使他们的数据更加公平（可查找、可互操作、可访问和可重用），以供更广泛的研究社区使用。然而，协调和支持这两种资源的跨队列互操作性的工作有限。在本文中，我们利用基于本体的方法来协调两个资源中的数据元素，并开发基于web的查询系统来搜索两个资源中的患者队列。我们首先跨NACC和ADNI映射数据元素，并对具有不一致允许值的映射数据元素执行值协调。在此基础上，构建了一个阿尔茨海默病数据元素本体（ADEO），对NACC和ADNI中的映射数据元素进行建模。我们进一步开发了一个原型跨队列查询系统来搜索NACC和ADNI的患者队列。经过人工检查，我们发现了172个NACC和ADNI之间的映射。这172个映射进一步用于构建ADEO中的公共概念。我们的数据元素映射和协调产生了五个文件，分别存储公共概念、NACC和ADNI中的变量、变量和公共概念之间的映射、分类类型数据元素的允许值以及编码不一致性协调。我们的跨队列查询系统由三个核心架构元素组成：基于web的界面、高级查询引擎和后端MongoDB数据库。在这项工作中，ADEO被专门设计用于促进NACC和ADNI数据资源的数据协调和跨队列查询。虽然我们的原型跨队列查询系统是为探索NACC和ADNI而开发的，但它的后端和前端框架已经被设计和实现为普遍适用于从多个异构数据源查询患者队列的其他领域。
In the United States, the National Alzheimer’s Coordinating Center (NACC) and the Alzheimer’s Disease Neuroimaging Initiative (ADNI) are two major data sharing resources for Alzheimer’s Disease (AD) research. NACC and ADNI strive to make their data more FAIR (findable, interoperable, accessible and reusable) for the broader research community. However, there is limited work harmonizing and supporting cross-cohort interoperability of the two resources. In this paper, we leverage an ontology-based approach to harmonize data elements in the two resources and develop a web-based query system to search patient cohorts across the two resources. We first mapped data elements across NACC and ADNI, and performed value harmonization for the mapped data elements with inconsistent permissible values. Then we built an Alzheimer’s Disease Data Element Ontology (ADEO) to model the mapped data elements in NACC and ADNI. We further developed a prototype cross-cohort query system to search patient cohorts across NACC and ADNI. After manual review, we found 172 mappings between NACC and ADNI. These 172 mappings were further used to construct common concepts in ADEO. Our data element mapping and harmonization resulted in five files storing common concepts, variables in NACC and ADNI, mappings between variables and common concepts, permissible values of categorical type data elements, and coding inconsistency harmonization, respectively. Our cross-cohort query system consists of three core architectural elements: a web-based interface, an advanced query engine, and a backend MongoDB database. In this work, ADEO has been specifically designed to facilitate data harmonization and cross-cohort query of NACC and ADNI data resources. Although our prototype cross-cohort query system was developed for exploring NACC and ADNI, its backend and frontend framework has been designed and implemented to be generally applicable to other domains for querying patient cohorts from multiple heterogeneous data sources.
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