ACS Brief
Surgeons Discuss How Machine Learning Models Can Predict Pediatric SSI Risk Using Preoperative Data
April 28, 2026
In the latest episode of The Operative Word from JACS, host Dr. Tom Varghese is joined by Drs. Carrie Chan and Karthik Balakrishnan, who discuss their recent JACS article, “Development, Validation, and Comparison of Machine Learning Models for Predicting Pediatric Surgical Site Infections Using the NSQIP-P Database.” Representing the largest study to date on predicting pediatric surgical site infections (SSI), the authors developed machine-learning models and ultimately recommended a regularized logistic regression model for clinical integration, balancing performance and feasibility for implementation. Findings support using routine preoperative data for personalized infection prevention and preoperative planning.
