ArticleJournal of the American Medical Informatics Association : JAMIA2019
Integrating ontologies of human diseases, phenotypes, and radiological diagnosis.
Article in Journal of the American Medical Informatics Association : JAMIA, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers, 1 of them a synthesis that pooled it.
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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
4 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Ontologies for Liver Diseases Representation: A Systematic Literature Review.Journal of digital imaging · 2020Pooled it
- Strategies and foundations for scientific discovery in longitudinal studies of bipolar disorder.Bipolar disorders · 2022Review
- Integrating Biological and Radiological Data in a Structured Repository: a Data Model Applied to the COSMOS Case Study.Journal of digital imaging · 2022Article
- Biomedical Ontologies to Guide AI Development in Radiology.Journal of digital imaging · 2021Review
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Mappings between ontologies enable reuse and interoperability of biomedical knowledge. The Radiology Gamuts Ontology (RGO)-an ontology of 16 918 diseases, interventions, and imaging observations-provides a resource for differential diagnosis and automated textual report understanding in radiology. An automated process with subsequent manual review was used to identify exact and partial matches of RGO entities to the Disease Ontology (DO) and the Human Phenotype Ontology (HPO). Exact mappings identified equivalent concepts; partial mappings identified subclass and superclass relationships. A total of 7913 distinct RGO entities (46.8%) were mapped to one or both of the two target ontologies. Integration of RGO's causal knowledge resulted in 9605 axioms that expressed direct causal relationships between DO diseases and HPO phenotypic abnormalities, and allowed one to formulate queries about causal relations using the abstraction properties in those two ontologies. The mappings can be used to support automated diagnostic reasoning, data mining, and knowledge discovery.
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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.