Prediction of incident atrial fibrillation in post-stroke patients using machine learning: a French nationwide study.
Arnaud BissonYassine LemriniWahbi El-BouriAlexandre BodinDenis AngoulvantGregory Y H LipLaurent FauchierPublished in: Clinical research in cardiology : official journal of the German Cardiac Society (2022)
ML algorithms predict incident AF post-stroke with a better ability than previously developed clinical scores. AF: atrial fibrillation; DNN: deep neural network; IS: ischemic stroke; KNN: K-nearest neighbors; LR: logistic regression; RFC: random forest classifier; XGBoost: extreme gradient boosting.
Keyphrases
- atrial fibrillation
- neural network
- oral anticoagulants
- catheter ablation
- left atrial
- left atrial appendage
- end stage renal disease
- direct oral anticoagulants
- cardiovascular disease
- climate change
- heart failure
- ejection fraction
- machine learning
- chronic kidney disease
- newly diagnosed
- peritoneal dialysis
- prognostic factors
- percutaneous coronary intervention
- deep learning
- patient reported