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NEURO-ONCOLOGY • NOVEMBER 2017 racy, precision, recall, and F1 measure were calculated for the NLP method. RESULTS: The manual review was completed in 27 days; mean discordance rate was 36.2%. The overall accuracy of the best logistic regression classifier was 84.6% on the training data and 82.8% on the hold-out test data. The precision and recall were both 83%, and the F1-score was 0.83. The algorithm required 28.9 minutes for training, after which it is able to classify the entire dataset in a less than 0.2 second. The model was incorporated into a user-friendly interface that allows for future report classification. CONCLUSION: NLP is a powerful method for the high-throughput evaluation of free-text radiology reports that can have high sensitivity and specificity. Use of NLP can accelerate retrospective clinical research with improved accuracy over manual chart review.

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