Systematic evaluation of machine learning models for postoperative surgical site infection prediction.

Background: Surgical site infections (SSIs) lead to increased mortality and morbidity, as well as increased healthcare costs. Multiple models for the prediction of this serious surgical complication have been developed, with an increasing use of machine learning (ML) tools.

Objective: The aim of this systematic review was to assess the performance as well as the methodological quality of validated ML models for the prediction of SSIs.