Integrating Clinical, Genetic, and Electrocardiogram-Based Artificial Intelligence to Estimate Risk of Incident Atrial Fibrillation.
Shinwan KanyJoel T RämöSamuel F FriedmanLu-Chen WengCarolina RoselliMin Seo KimAkl C FahedSteven A LubitzMahnaz MaddahPatrick T EllinorShaan KhurshidPublished in: medRxiv : the preprint server for health sciences (2024)
Integration of clinical, genetic, and AI-derived risk signals improves discrimination of 5-year AF risk over individual components. Models such as Predict-AF3 have substantial potential to improve prioritization of individuals for AF screening and preventive interventions.
Keyphrases
- artificial intelligence
- atrial fibrillation
- machine learning
- big data
- deep learning
- left atrial
- oral anticoagulants
- genome wide
- catheter ablation
- left atrial appendage
- cardiovascular disease
- heart failure
- direct oral anticoagulants
- physical activity
- risk assessment
- coronary artery disease
- acute coronary syndrome
- left ventricular
- mitral valve