Here, we developed and validated a highly generalizable natural language processing
algorithm based on deep-learning. Our algorithm was trained and tested on a highly diverse
dataset from over 2,000 hospital sites and 500 radiologists. The resulting algorithm achieved an
AUROC of 0.96 for the presence or absence of liver lesions while achieving a specificity of 0.99
and a sensitivity of 0.6.
Development of a Natural Language Processing Algorithm for the Classification of Suspicious Liver Lesions from Radiology Reports