Machine learning for subtype definition and risk prediction in heart failure, acute coronary syndromes and atrial fibrillation: systematic review of validity and clinical utility.
Amitava BanerjeeSuliang ChenGhazaleh FatemifarMohamad ZeinaR Thomas LumbersJohanna MielkeSimrat GillDipak KotechaDaniel F FreitagSpiros DenaxasHarry HemingwayPublished in: BMC medicine (2021)
Studies of ML in HF, ACS and AF are limited by number and type of included covariates, ML methods, population size, country, clinical setting and focus on single diseases, not overlap or multimorbidity. Clinical utility and implementation rely on improvements in development, validation and impact, facilitated by simple checklists. We provide clear steps prior to safe implementation of machine learning in clinical practice for cardiovascular diseases and other disease areas.
Keyphrases
- machine learning
- atrial fibrillation
- acute coronary syndrome
- heart failure
- systematic review
- clinical practice
- primary care
- percutaneous coronary intervention
- cardiovascular disease
- healthcare
- artificial intelligence
- quality improvement
- meta analyses
- oral anticoagulants
- big data
- acute heart failure
- catheter ablation
- antiplatelet therapy
- left atrial appendage
- left atrial
- direct oral anticoagulants
- deep learning
- left ventricular
- type diabetes
- mitral valve
- venous thromboembolism
- metabolic syndrome