Ontologizing Health Systems Data at Scale: Making Translational Discovery a Reality
| Autor: | Tiffany J. Callahan, Adrianne L. Stefanski, Jordan M. Wyrwa, Chenjie Zeng, Anna Ostropolets, Juan M. Banda, William A. Baumgartner, Richard D. Boyce, Elena Casiraghi, Ben D. Coleman, Janine H. Collins, Sara J. Deakyne Davies, James A. Feinstein, Asiyah Y. Lin, Blake Martin, Nicolas A. Matentzoglu, Daniella Meeker, Justin Reese, Jessica Sinclair, Sanya B. Taneja, Katy E. Trinkley, Nicole A. Vasilevsky, Andrew E. Williams, Xingmin A. Zhang, Joshua C. Denny, Patrick B. Ryan, George Hripcsak, Tellen D. Bennett, Melissa A. Haendel, Peter N. Robinson, Lawrence E. Hunter, Michael G. Kahn |
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| Přispěvatelé: | Callahan, Tiffany J [0000-0002-8169-9049], Wyrwa, Jordan M [0000-0002-5455-5859], Casiraghi, Elena [0000-0003-2024-7572], Collins, Janine H [0000-0002-8716-3261], Deakyne Davies, Sara J [0000-0002-8602-1624], Martin, Blake [0000-0001-5683-8310], Taneja, Sanya B [0000-0003-1707-1617], Williams, Andrew E [0000-0002-0692-412X], Robinson, Peter N [0000-0002-0736-9199], Kahn, Michael G [0000-0003-4786-6875], Apollo - University of Cambridge Repository |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
FOS: Computer and information sciences
J.3 Computer Science - Artificial Intelligence 42 Health Sciences Medicine (miscellaneous) 3 Good Health and Well Being Databases (cs.DB) Health Informatics Open Biomedical Ontologies 4203 Health Services and Systems Interoperability Mappings Computer Science Applications Semantic Alignment Artificial Intelligence (cs.AI) Networking and Information Technology R&D (NITRD) Computer Science - Databases Health Information Management Genetics Patient Safety OMOP |
| DOI: | 10.5281/zenodo.7067383 |
| Popis: | 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. This work was supported by funding from the National Library of Medicine (T15LM009451) to Lawrence E. Hunter and (T15LM007079) to George Hripcsak. |
| Databáze: | OpenAIRE |
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