Machine learning model flags patients with high risk of surgical complications

Improving the health of high-risk patients before their surgeries can lower mortality rates and cut healthcare costs. [Image by Gorodenkoff via Adobe Stock]

A newly developed machine learning model for surgical patients is automatically flagging those at high risk of complications to improve their odds of survival and reduce healthcare system costs.

Each day, the software reviews electronic medical records for patients scheduled for surgery and identifies those who might benefit from individualized coordinated care or prehabilitation to improve surgical results.

Researchers and physicians at the University of Pittsburgh and University of Pittsburgh Medical Center (UPMC) trained their algorithm on medical records for more than 1.2 million surgical patients. To help predict whether patients might suffer from complications after surgery, they focused the model on deaths from strokes, heart attacks and other…

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