Machine learning seen speeding diagnoses

Researchers at Regenstrief Institute and Indiana University School of Informatics and Computing say they now can detect cancer cases using data from free-text pathology reports at least as well—and faster—than clinicians reviewing reports manually. The researchers used existing data algorithms and open source machine learning tools to create a breakthrough electronic approach that could significantly speed patient diagnoses and public health reporting. “We think that it’s no longer necessary for humans to spend time reviewing text reports to determine if cancer is present or not,” says Shaun Grannis, MD, senior study author and interim director of the Regenstrief Center of Biomedical Informatics. Machine learning, which in healthcare is most frequently associated with initiatives using IBM’s Watson Health technology, uses established algorithms to find meaningful patterns in data automatically, and then…


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