Postoperative complications after cardiac surgery can be severe and even fatal, making it a
high-risk procedure. Predicting surgical risk can guide the effective formulation of treatment …

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 …

Method All patients in Golden Jubilee National Hospital undergoing cardiac surgery
between 1 st April 2012 and 31 st March 2016, reported in clinical audit dataset CaTHI, were …

Modern machine learning (ML) methods have revolutionised many industries, however
healthcare has been slow to realise the potential of these technologies. Cardiac surgery is …

Background The clinical applicability of machine learning predictions of patient outcomes
following cardiac surgery remains unclear. We applied machine learning to predict patient …

Preoperative risk assessment is essential for shared decision-making and adequate
perioperative care. Common scores provide limited predictive quality and lack personalized …

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 …

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 …

Machine learning (ML) is increasingly applied to predict adverse postoperative outcomes in
cardiac surgery. Commonly used ML models fail to translate to clinical practice due to …