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Communication of follow-up recommendations when abnormalities are identifiedon imaging studies is prone to error. In this paper, we present a naturallanguage processing approach based on deep learning to automatically identifyclinically important recommendations in radiology reports. Our approach firstidentifies the recommendation sentences and then extracts reason, test, andtime frame of the identified recommendations. To train our extraction models,we created a corpus of 567 radiology reports annotated for recommendationinformation. Our extraction models achieved 0.92 f-score for recommendationsentence, 0.65 f-score for reason, 0.73 f-score for test, and 0.84 f-score fortime frame. We applied the extraction models to a set of over 3.3 millionradiology reports and analyzed the adherence of follow-up recommendations.

Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset