Charles E Kahn1. 1. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, 3400 Spruce Street, 1 Silverstein, Philadelphia, PA 19104, USA, Tel: +1 215-662-3032, Fax: +1 215-662-7011 charles.kahn@uphs.upenn.edu.
Abstract
PURPOSE: The author sought to integrate an ontology of rare diseases with a large ontological model of radiological diagnosis. MATERIALS AND METHODS: The Orphanet Rare Disease Ontology (ORDO) comprised 6794 rare diseases. The Radiology Gamuts Ontology (RGO) incorporated 16 197 terms and 53,425 causal relations linking disorders to imaging manifestations. Semi-automated string-matching was used to match ORDO terms to RGO terms. RESULTS: Of 6794 ORDO terms, 1587 (23.3%) were matched to RGO terms. An additional 700 ORDO terms whose names were hyphenated lists of phenotypic features were added to RGO with causal links from the disease name to the various features. Matched terms were more likely to have higher disease prevalence. CONCLUSIONS: Integrating these ontologies expanded the set of terms and scope of knowledge available for radiological differential diagnosis, and can support translational rare-disease research by linking knowledge of genetics and imaging phenotypes.
PURPOSE: The author sought to integrate an ontology of rare diseases with a large ontological model of radiological diagnosis. MATERIALS AND METHODS: The Orphanet Rare Disease Ontology (ORDO) comprised 6794 rare diseases. The Radiology Gamuts Ontology (RGO) incorporated 16 197 terms and 53,425 causal relations linking disorders to imaging manifestations. Semi-automated string-matching was used to match ORDO terms to RGO terms. RESULTS: Of 6794 ORDO terms, 1587 (23.3%) were matched to RGO terms. An additional 700 ORDO terms whose names were hyphenated lists of phenotypic features were added to RGO with causal links from the disease name to the various features. Matched terms were more likely to have higher disease prevalence. CONCLUSIONS: Integrating these ontologies expanded the set of terms and scope of knowledge available for radiological differential diagnosis, and can support translational rare-disease research by linking knowledge of genetics and imaging phenotypes.