… The use of Big Data and machine learning (ML) offers considerable advantages for collection
… The primary aim of our study was to assess the main perioperative outcomes in which ML …

… • This systematic review and meta-analysis identified 103 studies that employed artificial
intelligence or machine learning to predict perioperative outcomes, but the overall quality was …

… A single perioperative outcome was described by Stehrer et al. In this study, the authors
were correlated actual perioperative blood loss to predicted perioperative blood loss with …

… To assess machine learning (ML) models for predicting postoperative complications using
… patient risks and help in anticipatory management for perioperative contingency planning. …

… learning (SL), and neural network learning (NNL). Addressing the challenges inherent in
predicting perioperative outcomes … care delivery throughout the perioperative journey [2,3,4]. …

… Within anesthesiology, perioperative EHRs have gone beyond simply providing enhanced
… 2 ; and more recently, predictive analytics using machine learning and waveform processing. …

… Machine learning is a promising tool to improve outcome prediction and … of machine learning
to predict pathology or response to treatment. A direct implementation of machine learning …

… machine learning methods within perioperative management of cardiac surgery patients.
The preferred reporting items for systematic reviews and meta-analyzes extension for scoping …

… All data for this study were extracted from the perioperative data warehouse (PDW), a custom
built, robust data warehouse containing all patients who have undergone surgery at UCLA …

… The machine learning models used in this study were originally trained and validated on
a retrospective cohort of approximately 110,000 adult patients who underwent surgery with …