  Radiological reports typically summarize the content and interpretation of
imaging studies in unstructured form that precludes quantitative analysis. This
limits the monitoring of radiological services to throughput undifferentiated
by content, impeding specific, targeted operational optimization. Here we
present Neuradicon, a natural language processing (NLP) framework for
quantitative analysis of neuroradiological reports. Our framework is a hybrid
of rule-based and artificial intelligence models to represent neurological
reports in succinct, quantitative form optimally suited to operational
guidance. We demonstrate the application of Neuradicon to operational
phenotyping of a corpus of 336,569 reports, and report excellent
generalizability across time and two independent healthcare institutions.
