Integrating Ontological Knowledge with Probability Data to Aid Diagnosis in Radiology

Abstract

Radiological diagnosis requires integration of imaging observations with patient factors, such as age, sex, and medical history. Imaging manifestations of a disease can be highly variable; conversely, one imaging finding may suggest several possible causes. To account for the inherent uncertainty of radiological diagnosis, this report explores the integration of probability data with an ontology of radiological diagnosis. The Radiology Gamuts Ontology (RGO) incorporates 16,839 entities that define diseases, interventions, and imaging observations of relevance to diagnostic radiology. RGO’s 55,564 causal (“may cause”) relationships link disorders and their potential imaging manifestations. From a cohort of 1.7 million radiology reports on more than 1.3 million patients, the frequency of individual RGO entities and of their pairwise co-occurrence was identified. These data allow estimation of conditional probabilities of pairs of entities. A user interface enables one to traverse the ontology’s network of causal relations with associated conditional-probability data. The system generates Bayesian network models that integrate an entity’s age and sex distribution with its causally related conditions.