OMOP2OBO

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Published September 10, 2022 | Version V2.0.1
Preprint Open

Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality

Creators

  • 1. University of Colorado Anschutz Medical Campus; Columbia University Irving Medical Center
  • 2. University of Colorado Anschutz Medical Campus
  • 3. National Institutes of Health
  • 4. Columbia University Irving Medical Center
  • 5. Georgia State University
  • 6. University of Pittsburgh School of Medicine
  • 7. Università degli Studi di Milano
  • 8. The Jackson Laboratory for Genomic Medicine
  • 9. University of Cambridge
  • 10. Children's Hospital Colorado
  • 11. University of Colorado Anschutz School of Medicine
  • 12. University of Colorado School of Medicine
  • 13. Semanticly
  • 14. University of Southern California
  • 15. Lawrence Berkeley National Laboratory
  • 16. HealthLinc
  • 17. University of Pittsburgh
  • 18. University of Colorado Anschutz Skaggs School of Pharmacy and Pharmaceutical Sciences and School of Medicine
  • 19. Tufts University
  • 20. Sema4
  • 21. Janssen Research and Development

Description

Common data models solve many challenges of standardizing electronic health record (EHR) data, but are unable to semantically integrate the resources needed for deep phenotyping. Open Biological and Biomedical Ontology (OBO) Foundry ontologies provide semantically computable representations of biological knowledge and enable the integration of a variety of biomedical data. However, mapping EHR data to OBO Foundry ontologies requires significant manual curation and domain expertise. We introduce a framework for mapping Observational Medical Outcomes Partnership (OMOP) standard vocabularies to OBO Foundry ontologies. Using this framework, we produced mappings for 92,367 conditions, 8,615 drug ingredients, and 10,673 measurement results. Mapping accuracy was verified by domain experts and when examined across 24 hospitals, the mappings covered 99% of conditions and drug ingredients and 68% of measurements. Finally, we demonstrate that OMOP2OBO mappings can aid in the systematic identification of undiagnosed rare disease patients who might benefit from genetic testing.

Notes

This work was supported by funding from the National Library of Medicine (T15LM009451) to Lawrence E. Hunter and (T15LM007079) to George Hripcsak.

Files

OMOP2OBO_Manuscript_v2.pdf

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Additional details

Related works

References
Software: https://github.com/callahantiff/OMOP2OBO (URL)