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 Biomedical Data Science and colleagues.
“Systematic attempts to curb unnecessary imaging for PE evaluation have focused on the use of existing predictive PE risk scoring tools, such as Wells or rGeneva, as CDS tools to inform the decision to perform advanced imaging, but in practice have had a disappointing influence on CT imaging yield or use,” the authors wrote. However, they went on to say that their method is different and “might be used as an automated clinical decision-support tool for patients referred for CT PE imaging to improve CT use.”
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