Background Machine learning (ML) models are promising tools for predicting adverse
postoperative outcomes in cardiac surgery, yet have not translated to routine clinical use …

Background Interest in the usefulness of machine learning (ML) methods for outcomes
prediction has continued to increase in recent years. However, the advantage of advanced …

Aims Preoperative risk assessment is crucial for cardiac surgery. Although previous studies
suggested machine learning (ML) may improve in-hospital mortality predictions after cardiac …

OBJECTIVES Interest in the clinical usefulness of machine learning for risk prediction has
bloomed recently. Cardiac surgery patients are at high risk of complications and therefore …

OBJECTIVES To perform a systematic comparison of in-hospital mortality risk prediction post-
cardiac surgery, between the predominant scoring system—European System for Cardiac …

Importance A variety of perioperative risk factors are associated with postoperative mortality
risk. However, the relative contribution of routinely collected intraoperative clinical …

Objective: This study aims to construct and validate several machine learning (ML)
algorithms to predict long-term mortality and identify risk factors in unselected patients post …

Background We developed machine learning models that combine preoperative and
intraoperative risk factors to predict mortality after cardiac surgery. Methods Machine …

Background 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 …

Objectives There is an ongoing debate over whether a procedural specific (eg Society of
Thoracic Surgeons (STS)) or universal model (eg EuroSCORE II (ES II)) should be used for …