ArticleNPJ digital medicine2023
Ontologizing health systems data at scale: making translational discovery a reality.
Article in NPJ digital medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- Advancing Patient-Centred AI With Adaptive Machine Learning.Cancer medicine · 2026Review
- OntoCodex: a multi-agent biomedical ontology enrichment framework.npj health systems · 2026Article
- Developing a Specialized Dravet Syndrome Ontology for Rare Disease Informatics and AI Applications.medRxiv : the preprint server for health sciences · 2026Article
- PheBee: A Graph-Aware System for Scalable, Traceable, and Semantic Phenotyping.medRxiv : the preprint server for health sciences · 2026Article
- The missing link: electronic health record linkage across species offers opportunities for improving One Health.JAMIA open · 2025Article
- A scalable approach for genomic-first rare disorder detection in a healthcare-based population.American journal of human genetics · 2025Article
- Radiomics Quality Score 2.0: towards radiomics readiness levels and clinical translation for personalized medicine.Nature reviews. Clinical oncology · 2025Review
- Comparative analysis of statistical and deep learning-based multi-omics integration for breast cancer subtype classification.Journal of translational medicine · 2025Article
- Increased discoverability of rare disease datasets through knowledge graph integration.JAMIA open · 2025Article
- Implications of mappings between International Classification of Diseases clinical diagnosis codes and Human Phenotype Ontology terms.JAMIA open · 2024Article
- Towards automated phenotype definition extraction using large language models.Genomics & informatics · 2024Article
- Clinical Research Informatics: a Decade-in-Review.Yearbook of medical informatics · 2024Review
- Symptom-BERT: Enhancing Cancer Symptom Detection in EHR Clinical Notes.Journal of pain and symptom management · 2024Article
- Converting OMOP CDM to phenopackets: A model alignment and patient data representation evaluation.Journal of biomedical informatics · 2024Article
- Association of post-COVID phenotypic manifestations with new-onset psychiatric disease.Translational psychiatry · 2024Article
- The Human Phenotype Ontology in 2024: phenotypes around the world.Nucleic acids research · 2024Article
- Predictive models of long COVID.EBioMedicine · 2023Article
- Generalisable long COVID subtypes: findings from the NIH N3C and RECOVER programmes.EBioMedicine · 2023Article
- The Vertebrate Breed Ontology: Toward Effective Breed Data Standardization.Journal of veterinary internal medicineArticle
Corrections and comments
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Authors and funding
32 authors.
Funding
Abstract
Common data models solve many challenges of standardizing electronic health record (EHR) data but are unable to semantically integrate all of 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. We introduce OMOP2OBO, an algorithm for mapping Observational Medical Outcomes Partnership (OMOP) vocabularies to OBO ontologies. 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. By aligning OMOP vocabularies to OBO ontologies our algorithm presents new opportunities to advance EHR-based deep phenotyping.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.