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

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 …

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 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 …

Background The benefits of cardiac surgery are sometimes difficult to predict and the
decision to operate on a given individual is complex. Machine Learning and Decision Curve …

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

Introduction In patients undergoing high‐risk cardiac surgery, the uncertainty of outcome
may complicate the decision process to intervene. To augment decision‐making, a machine …

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 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 …