Development of a Natural Language Processing Algorithm for the Classification of Suspicious Liver Lesions from Radiology Reports
Johnson, J.; Senevirathne, K.; Ngo, L.
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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.
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