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Though sophisticated algorithms have been developed for the classification of free-text radiology reports for pulmonary embolism (PE), their overall generalizability remains unvalidated given limitations in sample size and data homogeneity. We developed and validated a highly generalizable deep-learning based NLP algorithm for this purpose with data sourced from over 2,000 hospital sites and 500 radiologists. The algorithm achieved an AUCROC of 0.995 on chest angiography studies and 0.994 on non-angiography studies for the presence or absence of PE. The high accuracy achieved on this large and heterogeneous dataset allows for the possibility of application in large multi-center radiology practices as well as for deployment at novel sites without significant degradation in performance.

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