This study sought to use ontology-based knowledge to identify patients with rare diseases and to estimate the frequency of those diseases in a large database of radiology reports. Natural language processing methods were applied to 12,377,743 narrarive-text radiology reports of 7,803,811 patients at an academic health system. Using knowledge from the Orphanet Rare Disease Ontology and Radiology Gamuts Ontology, 1,154 of 6,794 rare diseases (17.0%) were observed in a total of 237,840 patients (3.05%). Ninety of 2,129 diseases (4%) with known prevalence less than 1 per 1,000,000 were observed in the database, whereas 100 of 173 diseases (58%) with prevalence greater than 1 per 10,000 were observed; the difference was statistically significant (p &lt; .00001). Automated ontology-based search of radiology reports can estimate the frequency of rare diseases, and those diseases with higher known prevalence were significantly more likely to appear in radiology reports.
Biological Ontologies Humans