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Institution-Specific Machine Learning Models Improve Mortality Risk Prediction for Cardiac Surgery Patients

https://doi.org/10.2139/ssrn.4180507
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Use of a Neural Network as a Predictive Instrument for Length of Stay in the Intensive Care Unit Following Cardiac Surgery
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Trends and Outcomes of Aortic Valve Replacement in Patients With End-Stage Renal Disease on Hemodialysis
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What is the Future of Mortality Prediction Models in Heart Valve Surgery?
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The Society of Thoracic Surgeons 2008 Cardiac Surgery Risk Models: Part 1—Coronary Artery Bypass Grafting Surgery
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The Society of Thoracic Surgeons 2008 Cardiac Surgery Risk Models: Part 2—Isolated Valve Surgery
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Perioperative Deaths After Mitral Valve Operations May Be Overestimated by Contemporary Risk Models
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Evaluation of The Society of Thoracic Surgeons Online Risk Calculator for Assessment of Risk in Patients Presenting for Aortic Valve Replacement After Prior Coronary Artery Bypass Graft: An Analysis Using the STS Adult Cardiac Surgery Database
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Enhancing the Value of Population-Based Risk Scores for Institutional-Level Use
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Artificial Intelligence and Machine Learning in Cardiovascular Health Care
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Predictive Utility of a Machine Learning Algorithm in Estimating Mortality Risk in Cardiac Surgery
inferredcites →https://doi.org/10.1016/j.athoracsur.2020.05.107openalex-extracted
Performance of a Machine Learning Algorithm in Predicting Outcomes of Aortic Valve Replacement
inferredcites →https://doi.org/10.1016/j.athoracsur.2021.03.043openalex-extracted
STS Adult Cardiac Surgery Database: 2021 Update on Outcomes, Quality, and Research
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Risk factor identification and mortality prediction in cardiac surgery using artificial neural networks
inferredcites →https://doi.org/10.1016/j.jtcvs.2012.07.055openalex-extracted
Does the Society of Thoracic Surgeons risk score accurately predict operative mortality for patients with pulmonary hypertension?
inferredcites →https://doi.org/10.1016/j.jtcvs.2013.06.042openalex-extracted
Reliability of new scores in predicting perioperative mortality after mitral valve surgery
inferredcites →https://doi.org/10.1016/j.jtcvs.2013.08.036openalex-extracted
Outcomes of surgical aortic valve replacement in moderate risk patients: Implications for determination of equipoise in the transcatheter era
inferredcites →https://doi.org/10.1016/j.jtcvs.2020.07.105openalex-extracted
Machine learning improves mortality risk prediction after cardiac surgery: Systematic review and meta-analysis
inferredcites →https://doi.org/10.1016/j.jtcvs.2020.08.091openalex-extracted
Commentary: Dabblers: Beware of hidden dangers in machine-learning comparisons
inferredcites →https://doi.org/10.1016/s1010-7940(99)00134-7openalex-extracted
European system for cardiac operative risk evaluation (EuroSCORE)
inferredcites →https://doi.org/10.1016/s2589-7500(20)30217-xopenalex-extracted
Clinical features of COVID-19 mortality: development and validation of a clinical prediction model
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Gene Selection for Cancer Classification using Support Vector Machines
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Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports
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Classification evaluation
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Objective risk stratification of prostate cancer using machine learning and radiomics applied to multiparametric magnetic resonance images
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Stopping versus continuing acetylsalicylic acid before coronary artery bypass surgery: A systematic review and meta-analysis of 14 randomized controlled trials with 4499 patients
inferredcites →https://doi.org/10.1093/eurheartj/ehaa1065openalex-extracted
The year in cardiovascular medicine 2020: digital health and innovation
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Multicenter Comparison of Machine Learning Methods and Conventional Regression for Predicting Clinical Deterioration on the Wards
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A New Bayesian Network-Based Risk Stratification Model for Prediction of Short-Term and Long-Term LVAD Mortality
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Prediction of In‐hospital Mortality in Emergency Department Patients With Sepsis: A Local Big Data–Driven, Machine Learning Approach
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Predicting adverse outcomes of cardiac surgery with the application of artificial neural networks
inferredcites →https://doi.org/10.1145/2939672.2939785openalex-extracted
XGBoost
inferredcites →https://doi.org/10.1161/jaha.120.016282openalex-extracted
Outcomes of Reoperative Coronary Artery Bypass Graft Surgery in the United States
inferredcites →https://doi.org/10.1177/1556984520971775openalex-extracted
Current Surgical Risk Scores Overestimate Risk in Minimally Invasive Aortic Valve Replacement
inferredcites →https://doi.org/10.1186/s12916-019-1426-2openalex-extracted
Key challenges for delivering clinical impact with artificial intelligence
inferredcites →https://doi.org/10.1371/journal.pone.0111264openalex-extracted
Cardiac Health Risk Stratification System (CHRiSS): A Bayesian-Based Decision Support System for Left Ventricular Assist Device (LVAD) Therapy
inferredcites →https://doi.org/10.1371/journal.pone.0118432openalex-extracted
The Precision-Recall Plot Is More Informative than the ROC Plot When Evaluating Binary Classifiers on Imbalanced Datasets
inferredcites →https://doi.org/10.1371/journal.pone.0145395openalex-extracted
Neural Network Prediction of ICU Length of Stay Following Cardiac Surgery Based on Pre-Incision Variables
inferredcites →https://doi.org/10.1371/journal.pone.0169772openalex-extracted
A Comparison of a Machine Learning Model with EuroSCORE II in Predicting Mortality after Elective Cardiac Surgery: A Decision Curve Analysis
inferredcites →https://doi.org/10.1109/jproc.2015.2501978openalex-snapshot
Machine Learning and Decision Support in Critical Care
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