Machine learning can avoid unnecessary CT use in PE patients

A neural network model can scour electronic medical record (EMR) data and determine if a patient has imaging-specific pulmonary embolism (PE)—a potential remedy for unnecessary CT imaging, reported authors of a multicenter study published in JAMA Network Open.
The machine learning platform—Pulmonary Embolism Result Forecast Model or PERFORM—converts raw EMR data, such as demographics, vital signs, medications and lab tests, into a PE risk score for patients referred for CT imaging. When trained and validated on more than 3,400 patients, PERFORM beat out all other existing PE risk scoring methods, according to Imon Banerjee, PhD, with Stanford University’s Department of…

Machine learning can avoid unnecessary CT use in PE patients
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