  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.
