Optimization of Using Multiple Machine Learning Approaches in Atrial Fibrillation Detection Based on a Large-Scale Data Set of 12-Lead Electrocardiograms: Cross-Sectional Study.
Beau Bo-Sheng ChuangAlbert C YangPublished in: JMIR formative research (2024)
-score of 0.988 and minimal interlead variation underscores the potential of machine learning algorithms to bolster real-time AF detection. This advancement could significantly improve patient care in intensive care units as well as facilitate remote monitoring through wearable devices, ultimately enhancing clinical outcomes.
Keyphrases
- machine learning
- atrial fibrillation
- big data
- artificial intelligence
- intensive care unit
- loop mediated isothermal amplification
- real time pcr
- deep learning
- left atrial
- heart failure
- oral anticoagulants
- electronic health record
- catheter ablation
- direct oral anticoagulants
- mechanical ventilation
- coronary artery disease
- left ventricular
- acute coronary syndrome
- risk assessment
- human health
- mitral valve
- acute respiratory distress syndrome