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 …

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

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

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 …

Background The objective of this study was to predict the mortality risk of patients during or
shortly after cardiac surgery by using machine learning techniques and their learning …

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

Objective Current cardiac surgery risk models do not address a substantial fraction of
procedures. We sought to create models to predict the risk of operative mortality for an …

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 Machine learning is a useful tool for predicting medical outcomes. This study
aimed to develop a machine learning–based preoperative score to predict cardiac surgical …

OBJECTIVES Machine learning methods potentially provide a highly accurate and detailed
assessment of expected individual patient risk before elective cardiac surgery. Correct …