Machine learning can understand text reports written by radiologists

Researchers from the Icahn School of Medicine at Mount Sinai have leveraged natural language processing algorithms to automatically identify clinical concepts in radiologist reports for computed tomography scans. Using more than 96,000 radiologist reports associated with head CT scans performed at The Mount Sinai Hospital and Mount Sinai Queens, researchers trained the computer software to understand text reports written by radiologists, achieving an accuracy of 91 percent. The NLP algorithms were used to teach the computer clusters of phrases, including words such as phospholipid, heartburn and colonoscopy. Results of the study were published this week in the journal Radiology. "The language used in radiology has a natural structure, which makes it amenable to machine learning," says senior author Eric Oermann, MD, an instructor in the Department of Neurosurgery at the Icahn School of Medicine.