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Computer Science > Databases

arXiv:2209.04732 (cs)
[Submitted on 10 Sep 2022 (v1), last revised 31 Jan 2023 (this version, v2)]

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

Authors:Tiffany J. Callahan, Adrianne L. Stefanski, Jordan M. Wyrwa, Chenjie Zeng, Anna Ostropolets, Juan M. Banda, William A. Baumgartner Jr., Richard D. Boyce, Elena Casiraghi, Ben D. Coleman, Janine H. Collins, Sara J. Deakyne-Davies, James A. Feinstein, Melissa A. Haendel, Asiyah Y. Lin, Blake Martin, Nicolas A. Matentzoglu, Daniella Meeker, Justin Reese, Jessica Sinclair, Sanya B. Taneja, Katy E. Trinkley, Nicole A. Vasilevsky, Andrew Williams, Xingman A. Zhang, Joshua C. Denny, Peter N. Robinson, Patrick Ryan, George Hripcsak, Tellen D. Bennett, Lawrence E. Hunter, Michael G. Kahn
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Abstract: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.
Comments: Supplementary Material is included at the end of the manuscript
Subjects: Databases (cs.DB); Artificial Intelligence (cs.AI)
ACM classes: J.3
Cite as: arXiv:2209.04732 [cs.DB]
  (or arXiv:2209.04732v2 [cs.DB] for this version)
  https://doi.org/10.48550/arXiv.2209.04732

Submission history

From: Tiffany Callahan [view email]
[v1] Sat, 10 Sep 2022 19:31:41 UTC (9,273 KB)
[v2] Tue, 31 Jan 2023 00:42:49 UTC (6,070 KB)
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