Accurate AI: Machine learning models identify findings in radiology reports

Machine learning models can identify key information in radiology reports with significant accuracy, according to a new study published in Radiology. The authors used more than 96,000 head CT reports for their research, turning to a bag-of-words (BOW) model to label a small subset of the reports and then allowing the trained algorithms to do the rest. BOW was used because it "discards grammar and context" and "utilizes document-level word occurrences as its features." Overall, machine learning algorithms were able to successfully identify findings in the reports. The model with the best results had a held out area under the receiver operating characteristic curve (AUC) of 0.966 for identifying critical head CT findings and an average AUC of 0.957 for all head CT findings.