New Biparietal Bipolar Catheter Prototype for Hybrid Atrial Fibrillation Ablation

Matteucci, Francesco; Maesen, Bart; Asmundis, Carlo de; Parise, Gianmarco; Micali, Linda Renata; Tuijthof, Gabriëlle J. M.; Gerits, Peter; Vernooy, Kevin; Meir, Mark La

doi:10.1177/1556984520981025

Cited by 1 publication

(1 citation statement)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This work has involved various professionals, including surgeons and cardiologists fully committed to diagnosing and treating AF, general physicians, data scientists, machine learning researchers, and modeling experts [ 34 , 35 , 36 , 37 , 38 , 39 , 40 , 41 ].…”

Section: Methodsmentioning

confidence: 99%

Machine Learning to Identify Patients at Risk of Developing New-Onset Atrial Fibrillation after Coronary Artery Bypass

Parise

Vaidyanathan

et al. 2023

JCDD

Self Cite

View full text Add to dashboard Cite

Background: This study aims to get an effective machine learning (ML) prediction model of new-onset postoperative atrial fibrillation (POAF) following coronary artery bypass grafting (CABG) and to highlight the most relevant clinical factors. Methods: Four ML algorithms were employed to analyze 394 patients undergoing CABG, and their performances were compared: Multivariate Adaptive Regression Spline, Neural Network, Random Forest, and Support Vector Machine. Each algorithm was applied to the training data set to choose the most important features and to build a predictive model. The better performance for each model was obtained by a hyperparameters search, and the Receiver Operating Characteristic Area Under the Curve metric was selected to choose the best model. The best instances of each model were fed with the test data set, and some metrics were generated to assess the performance of the models on the unseen data set. A traditional logistic regression was also performed to be compared with the machine learning models. Results: Random Forest model showed the best performance, and the top five predictive features included age, preoperative creatinine values, time of aortic cross-clamping, body surface area, and Logistic Euro-Score. Conclusions: The use of ML for clinical predictions requires an accurate evaluation of the models and their hyperparameters. Random Forest outperformed all other models in the clinical prediction of POAF following CABG.

show abstract