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

arXiv: https://arxiv.org/abs/2209.04732 · HTML: https://mlsanity.com/paper/2209.04732/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 146 references that are in the map.
- First posted on arXiv 10 Sep 2022.

## Builds on (most cited first, 3 of 3)

- [A Simple Standard for Sharing Ontological Mappings (SSSOM)](https://mlsanity.com/paper/2112.07051/) (2021)
- [Ontology Development Kit: a toolkit for building, maintaining, and standardising biomedical ontologies](https://mlsanity.com/paper/2207.02056/) (2022)
- [Semantic processing of EHR data for clinical research](https://mlsanity.com/paper/1511.03036/) (2015)

## Built on by (most cited first, 2 of 2)

- [ISPO: An Integrated Ontology of Symptom Phenotypes for Semantic Integration of Traditional Chinese Medical Data](https://mlsanity.com/paper/2407.12851/) (2024)
- [Representation Before Training: A Practical Benchmark for Generative Medical Event Model Tokenization](https://mlsanity.com/paper/2604.16775/) (2026)

## Abstract (arXiv)

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.