… In conclusion, we successfully applied the machine learning method to predict AKI after
cardiac surgery, which can be used to determine risks after surgery. We demonstrated that the …

… , especially when the benefit of surgery is difficult to assess and when … on machine learning
has bloomed in recent years. We found that prediction models based on machine learning …

… machine learning has been applied to certain aspects of health care, its potential use specifically
in cardiac surgery … a machine learning algorithm in estimating operative mortality risk in …

… This study found that, in cardiac surgery patients, ML models were not superior to currently
used statistical methods. These findings suggest that, until better technology is developed, …

… Cardiac surgery patients are at high risk of … of machine learning algorithms over traditional
logistic regression (LR) model to predict in-hospital mortality following cardiac surgery. …

… : 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-cardiac surgery. …

Background Machine learning (ML) is developing fast with promising prospects within
medicine and already has several applications in perioperative care. We conducted a scoping …

… a machine learning-based model with EuroSCORE II to predict mortality after elective cardiac
surgery… To the best of our knowledge, machine learning and decision curve analyses have …

… 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 abilities from …

… the ML model to a wider variety of cardiac surgical procedures—including those without …
surgery and aortic surgery. These operations, which represent a quarter of all of cardiac surgical …