Objective: The aim of this study was to quantify the variability of language in free text reports of pulmonary embolus (PE) studies and to gauge the informativeness of free text to predict PE diagnosis using machine learning as proxy for human understanding.
Materials And Methods: All 1,133 consecutive chest CTs with contrast studies performed under a PE protocol and ordered in the emergency department in 2016 were selected from our departmental electronic workflow system. We used commercial text-mining and predictive analytics software to parse and describe all report text and to generate a suite of machine learning rules that sought to predict the "gold standard" radiological diagnosis of PE.