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
- intensive care unit
- artificial intelligence
- loop mediated isothermal amplification
- deep learning
- real time pcr
- label free
- electronic health record
- left atrial appendage
- left atrial
- catheter ablation
- heart rate
- percutaneous coronary intervention
- direct oral anticoagulants
- human health
- risk assessment
- venous thromboembolism
- sensitive detection
- acute respiratory distress syndrome
- extracorporeal membrane oxygenation