Predicting target lesion failure following percutaneous coronary intervention through machine learning risk assessment models.
Mamas Andreas MamasMarco RoffiOle FröbertAlaide ChieffoAlessandro BeneduceAndrija MatetićPim A L ToninoDragica PaunovicLotte JacobsRoxane DebrusJérémy El AissaouiFrank van LeeuwenEvangelos KontopantelisPublished in: European heart journal. Digital health (2023)
Clinicaltrial.gov identifier is NCT02188355.
Keyphrases
- percutaneous coronary intervention
- risk assessment
- machine learning
- st segment elevation myocardial infarction
- acute coronary syndrome
- st elevation myocardial infarction
- acute myocardial infarction
- coronary artery disease
- antiplatelet therapy
- coronary artery bypass grafting
- human health
- heavy metals
- artificial intelligence
- big data
- coronary artery bypass
- atrial fibrillation
- deep learning
- left ventricular
- heart failure