Development of a Machine Learning Model to Predict Outcomes and Cost After Cardiac Surgery

• Published in: The Annals of thoracic surgery Vol. 115; no. 6; pp. 1533 - 1542
• Main Authors: Zea-Vera, Rodrigo, Ryan, Christopher T., Navarro, Sergio M., Havelka, Jim, Wall, Matthew J., Coselli, Joseph S., Rosengart, Todd K., Chatterjee, Subhasis, Ghanta, Ravi K.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.06.2023
• Subjects: Aged; Cardiac Surgical Procedures - adverse effects; Coronary Artery Bypass - methods; Humans; Machine Learning; Medicare; Thoracic Surgery; United States - epidemiology; United States; PROMM; STS; PROM; XGBoost; AUC-PR; ROC; CABG; ML

Machine learning (ML) algorithms may enhance outcomes prediction and help guide clinical decision making. This study aimed to develop and validate a ML model that predicts postoperative outcomes and costs after cardiac surgery. The Society of Thoracic Surgeons registry data from 4874 patients who un...