A novel machine learning model to predict high on-treatment platelet reactivity on clopidogrel in Asian patients after percutaneous coronary intervention.
Lan-Ping DingPing LiLi-Rong YangMang-Mang PanMin ZhouChi ZhangYi-Dan YanHou-Wen LinXiao-Ye LiZhi-Chun GuPublished in: International journal of clinical pharmacy (2023)
A ML-based approach, such as XGBoost, showed optimum performance and might help predict HTPR on clopidogrel after PCI and guide clinical decision-making. Further validated studies will strengthen this finding.
Keyphrases
- percutaneous coronary intervention
- acute coronary syndrome
- antiplatelet therapy
- st segment elevation myocardial infarction
- acute myocardial infarction
- machine learning
- coronary artery disease
- st elevation myocardial infarction
- coronary artery bypass grafting
- end stage renal disease
- decision making
- ejection fraction
- newly diagnosed
- atrial fibrillation
- peritoneal dialysis
- prognostic factors
- coronary artery bypass
- artificial intelligence
- deep learning
- patient reported
- smoking cessation