Machine Learning Modeling to Predict Atrial Fibrillation Detection in Embolic Stroke of Undetermined Source Patients.
Chua MingGeraldine J W LeeYao Hao TeoYao Neng TeoEmma M S TohTony Y W LiChloe Yitian GuoJiayan DingXinyan ZhouHock Luen TeohSwee-Chong SeowLeonard Leong-Litt YeoChing-Hui SiaGregory Yoke Hong LipMehul MotaniBenjamin Yq TanPublished in: Journal of personalized medicine (2024)
Machine learning modeling incorporating clinical and echocardiographic variables predicted AF in ESUS patients with moderate accuracy.
Keyphrases
- atrial fibrillation
- machine learning
- ejection fraction
- left atrial
- end stage renal disease
- newly diagnosed
- oral anticoagulants
- chronic kidney disease
- catheter ablation
- artificial intelligence
- heart failure
- prognostic factors
- left atrial appendage
- left ventricular
- peritoneal dialysis
- mitral valve
- percutaneous coronary intervention
- high intensity
- deep learning
- subarachnoid hemorrhage
- blood brain barrier