Predicting serious postoperative complications and evaluating racial fairness in machine learning algorithms for metabolic and bariatric surgery.

Journal: Surgery for obesity and related diseases : official journal of the American Society for Bariatric Surgery

PMID: 39232870

BACKGROUND: Predicting the risk of complications is critical in metabolic and bariatric surgery (MBS).

Dong-Won Kang

Department of Biomedical Engineering, College of Health Science, Yonsei University, Wonju, Kangwon-do, 26493, South Korea.
Shouhao Zhou

Department of Public Health Sciences, Penn State College of Medicine, Hershey, Pennsylvania.
Russell Torres

Department of Information Technology and Decision Sciences, University of North Texas, Denton, Texas.
Abhinandan Chowdhury

Department of Mathematics, Savannah State University, Savannah, Georgia.
Suman Niranjan

Department of Logistics and Operations Management, G. Brint Ryan College of Business, University of North Texas, Denton, Texas, USA.
Ann Rogers

Department of Surgery, Penn State College of Medicine.
Chan Shen

Division of Outcomes Research and Quality, Department of Surgery, College of Medicine, Pennsylvania State University, Hershey, PA, USA. Electronic address: cshen@pennstatehealth.psu.edu.

Adult Algorithms Bariatric Surgery Female Humans Machine Learning Male Middle Aged Obesity, Morbid Postoperative Complications Risk Assessment United States

View on PubMed Access via DOI PubMed (39232870)