论文标题
将卫生系统数据大规模化：使翻译发现成为现实
Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality
论文作者
论文摘要
背景：常见数据模型解决了标准化电子健康记录（EHR）数据的许多挑战，但无法将其整合到深层表型所需的所有资源。开放的生物学和生物医学本体论（OBO）铸造本体论提供了可计算的生物学知识表示形式，并能够整合异质数据。但是，将EHR数据映射到OBO本体学需要大量的手动策展和领域专业知识。目的：我们将Omop2OBO介绍为一种用于将观察性医学结果伙伴关系（OMOP）词汇映射到OBO本体论的算法。结果：使用OMOP2OBO，我们为92,367条，8611种药物成分和10,673个测量结果制作了映射，在24个医院检查临床实践中，覆盖了68-99％的概念。当用于表型罕见病患者时，这些映射有助于系统地识别可能受益于基因检测的未诊断患者。结论：通过将OMOP词汇与OBO本体结合在一起，我们的算法为推进基于EHR的深层表型提供了新的机会。
Background: Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate all the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide computable representations of biological knowledge and enable the integration of heterogeneous data. However, mapping EHR data to OBO ontologies requires significant manual curation and domain expertise. Objective: We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. Results: Using OMOP2OBO, we produced mappings for 92,367 conditions, 8611 drug ingredients, and 10,673 measurement results, which covered 68-99% of concepts used in clinical practice when examined across 24 hospitals. When used to phenotype rare disease patients, the mappings helped systematically identify undiagnosed patients who might benefit from genetic testing. Conclusions: By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.
