Importance Postoperative complications can significantly impact perioperative care
management and planning. Objectives To assess machine learning (ML) models for …

Objective: To accurately calculate the risk for postoperative complications and death after
surgery in the preoperative period using machine-learning modeling of clinical data …

Importance Identifying patients at high risk of adverse outcomes prior to surgery may allow
for interventions associated with improved postoperative outcomes; however, few tools exist …

Objective: To compare the performance of machine learning models against the traditionally
derived Combined Assessment of Risk Encountered in Surgery (CARES) model and the …

Importance Predicting postoperative complications has the potential to inform shared
decisions regarding the appropriateness of surgical procedures, targeted risk-reduction …

Surgical complications pose significant challenges for surgeons, patients, and health care
systems as they may result in patient distress, suboptimal outcomes, and higher health care …

Background Early detection of postoperative complications, including organ failure, is pivotal
in the initiation of targeted treatment strategies aimed at attenuating organ damage. In an …

During the perioperative period patients often suffer complications, including acute kidney
injury (AKI), reintubation, and mortality. In order to effectively prevent these complications …

Background Surgical resection is the only potentially curative treatment for patients with
colorectal, liver, and pancreatic cancers. Although these procedures are performed with low …

Background Risk stratification plays a central role in anesthetic evaluation. The use of Big
Data and machine learning (ML) offers considerable advantages for collection and …