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Machine learning methods have recently achieved high-performance inbiomedical text analysis. However, a major bottleneck in the widespreadapplication of these methods is obtaining the required large amounts ofannotated training data, which is resource intensive and time consuming. Recentprogress in self-supervised learning has shown promise in leveraging large textcorpora without explicit annotations. In this work, we built a self-supervisedcontextual language representation model using BERT, a deep bidirectionaltransformer architecture, to identify radiology reports requiring promptcommunication to the referring physicians. We pre-trained the BERT model on alarge unlabeled corpus of radiology reports and used the resulting contextualrepresentations in a final text classifier for communication urgency. Our modelachieved a precision of 97.0%, recall of 93.3%, and F-measure of 95.1% on anindependent test set in identifying radiology reports for prompt communication,and significantly outperformed the previous state-of-the-art model based onword2vec representations.

Self-Supervised Contextual Language Representation of Radiology Reports to Improve the Identification of Communication Urgency