Machine learning-based identification of risk-factor signatures for undiagnosed atrial fibrillation in primary prevention and post-stroke in clinical practice.
Renate B SchnabelHenning WittJochen WalkerMarion LudwigBastian GeelhoedNils KossackMarie SchildRobert MillerPaulus F KirchhofPublished in: European heart journal. Quality of care & clinical outcomes (2022)
ICD-coded clinical variables selected by machine learning can improve the identification of patients at risk of newly diagnosed AF. Using this readily available, automatically coded information can target AF screening efforts to identify high-risk populations in primary care and stroke survivors.
Keyphrases
- atrial fibrillation
- machine learning
- primary care
- newly diagnosed
- clinical practice
- oral anticoagulants
- catheter ablation
- left atrial
- left atrial appendage
- direct oral anticoagulants
- heart failure
- artificial intelligence
- risk factors
- bioinformatics analysis
- big data
- percutaneous coronary intervention
- young adults
- deep learning
- quality improvement
- health information
- acute coronary syndrome
- coronary artery disease
- venous thromboembolism
- general practice
- blood brain barrier
- genetic diversity