Dies ist eine Übersichtsseite mit Metadaten zu dieser wissenschaftlichen Arbeit. Der vollständige Artikel ist beim Verlag verfügbar.
Machine learning and Regression–Based models for prediction of postoperative atrial fibrillation following coronary artery bypass grafting: A systematic review and meta-analysis
0
Zitationen
4
Autoren
2026
Jahr
Abstract
Machine learning models demonstrate moderate accuracy for predicting POAF after CABG but are limited by heterogeneity, methodological shortcomings, and restricted external validation. Further rigorously designed and prospectively validated studies are needed to support clinical implementation.
Ähnliche Arbeiten
Aspirin plus Clopidogrel as Secondary Prevention after Stroke or Transient Ischemic Attack: A Systematic Review and Meta-Analysis
2014 · 11.546 Zit.
Dabigatran versus Warfarin in Patients with Atrial Fibrillation
2009 · 11.135 Zit.
2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS)
2020 · 9.722 Zit.
2017 ESC Guidelines for the management of acute myocardial infarction in patients presenting with ST-segment elevation
2017 · 9.611 Zit.
Rivaroxaban versus Warfarin in Nonvalvular Atrial Fibrillation
2011 · 9.326 Zit.