An Ontology-Based Approach to Estimate the Frequency of Rare Diseases in Narrative-Text Radiology Reports
(0) 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<.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.
Information Storage and Retrieval Knowledge Bases Rare Diseases
Charles E.Kahn
Department of Radiology and Institute for Biomedical Informatics,University of Pennsylvania,Philadelphia,Pennsylvania,USA
国际会议
第十六届世界医药健康信息学大会((MEDINFO2017)、第二届世界医药健康信息学华语论坛(WCHIS 2017)、第15届全国医药信息学大会(CMIA 2017)
苏州
英文
896-900
2017-08-21（万方平台首次上网日期，不代表论文的发表时间）