Researchers Show That A Machine Learning Model Can Improve Mortality Risk Prediction For Cardiac Surgery Patients

A research team from Mount Sinai has developed a machine learning-based model that enables medical institutions to predict the mortality risk for individual cardiac surgery patients. This data-driven algorithm, built on troves of electronic health records (EHR), is the first institution-specific model for assessing a cardiac patient’s risk prior to surgery, allowing health care providers to pursue the best course of action for that individual. The team’s work was published in The Journal of Thoracic and Cardiovascular Surgery (JTCVS) Open. 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.