original ) (raw )Machine learning algorithms for predicting mortality after coronary artery bypass grafting
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Frontiers in Cardiovascular Medicine, 2022
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Scientific Reports, 2021
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JACC: Cardiovascular Interventions, 2019
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Journal of Advances in Medical and Biomedical Research
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Journal of Cardiothoracic and Vascular Anesthesia, 2021
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Journal of Electronics, Electromedical Engineering, and Medical Informatics, 2024
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The Cardiothoracic Surgeon
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State‐of‐the‐art machine learning improves predictive accuracy of 1‐year survival after heart transplantation
ANDREAS TZOUMAS
ESC Heart Failure, 2021
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Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients
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Healthcare
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Predicting the Risk of Complications in Coronary Artery Bypass Operations using Neural Networks
Richard Lippmann
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BMC Medical Informatics and Decision Making
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Prediction of All-Cause Mortality Following Percutaneous Coronary Intervention in Bifurcation Lesions Using Machine Learning Algorithms
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Journal of Personalized Medicine
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Multi-center validation of machine learning model for preoperative prediction of postoperative mortality
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npj Digital Medicine
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Mortality risk prediction in high-risk patients undergoing coronary artery bypass grafting: Are traditional risk scores accurate?
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PLOS ONE, 2021
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Clinical research Predicting reintubation, prolonged mechanical ventilation and death in post-coronary artery bypass graft surgery: a comparison between artificial neural networks and logistic regression models
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Archives of Medical Science, 2015
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Predicting two-year survival versus non-survival after first myocardial infarction using machine learning and Swedish national register data
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BMC Medical Informatics and Decision Making, 2017
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Comparison of artificial neural networks with logistic regression in prediction of in-hospital death after percutaneous transluminal coronary angioplasty
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American Heart Journal, 2000
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Multivariate Prediction ofIn-Hospital Mortality Associated WithCoronary Artery Bypass GraftSurgery
Joan Tryzelaar
2010
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Can machine learning unravel unsuspected, clinically important factors predictive of long-term mortality in complex coronary artery disease? A call for ‘big data’
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European Heart Journal - Digital Health
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Comparison of machine learning algorithms to predict length of hospital stay in patients undergoing heart bypass surgery
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2021 International Symposium on Biomedical Engineering and Computational Biology, 2021
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Pre-operative Machine Learning for Heart Transplant Patients Bridged with Temporary Mechanical Circulatory Support
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Journal of Cardiovascular Development and Disease
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Machine learning for prediction of all-cause mortality in patients with suspected coronary artery disease: a 5-year multicentre prospective registry analysis
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European heart journal, 2016
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Machine Learning Improves the Identification of Individuals With Higher Morbidity and Avoidable Health Costs After Acute Coronary Syndromes
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Value in Health, 2020
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Short-Term Risk Estimation and Treatment Planning for Cardiovascular Disease Patients after First Diagnostic Catheterizations with Machine Learning Models
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Applied Sciences
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Comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis
Tsuyoshi Sakama
PLOS ONE, 2019
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Comparative Analysis of Machine Learning Algorithms for Predicting the Risk of Recurrent Coronary Artery Disease within a 6-Month Post- Treatment Window
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Trends in telemedicine & E-health, 2024
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Random effects adjustment in machine learning models for cardiac surgery risk prediction: a benchmarking study
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A machine learning–based 1-year mortality prediction model after hospital discharge for clinical patients with acute coronary syndrome
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Health Informatics Journal, 2019
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Adverse Outcomes Prediction for Congenital Heart Surgery: A Machine Learning Approach
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Machine learning-based risk prediction of intrahospital clinical outcomes in patients undergoing TAVI
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Clinical Research in Cardiology, 2020
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Artificial Neural Networks Prognostic Evaluation of Post-Surgery Complications in Patients Underwent to Coronary Artery Bypass Graft Surgery
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2009 International Conference on Machine Learning and Applications, 2009
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