Mount Sinai researchers have developed a machine learning model that can predict the mortality risk of cardiac surgery patients.
The algorithm uses electronic health records to assess a cardiac patient's risk prior to surgery, which allows healthcare providers to individualize plans for that individual, according to a May 17 hospital news release shared with Becker's.
"The standard-of-care risk models used today are limited by their applicability to specific types of surgeries, leaving out significant numbers of patients undergoing complex or combination procedures for which no models exist," senior author Ravi Iyengar, PhD, director of the Mount Sinai Institute for Systems Biomedicine, said in the release. "Our team rigorously combined electronic health record data and machine learning methods to demonstrate for the first time how individual institutions can build their own risk models for post-cardiac surgery mortality."
The algorithm also incorporates information about the hospital's patient population including demographics, socioeconomic factors and health characteristics to provide a more holistic approach, the release said.
The research was published May 17 in The Journal of Thoracic and Cardiovascular Surgery Open.