Open AccessMachine learning-based long-term outcome prediction in patients undergoing percutaneous coronary intervention
Author(s) -
Shangyu Liu,
Shengwen Yang,
Anlu Xing,
Lihui Zheng,
Lishui Shen,
Bin Tu,
Yan Yao
Publication year - 2021
Publication title -
cardiovascular diagnosis and therapy
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.83
H-Index - 22
eISSN - 2223-3660
pISSN - 2223-3652
DOI - 10.21037/cdt-21-37
Subject(s) - conventional pci , medicine , percutaneous coronary intervention , coronary artery disease , decision tree , gradient boosting , random forest , logistic regression , cardiology , receiver operating characteristic , machine learning , myocardial infarction , computer science
Traditional prognostic risk assessment in patients with coronary artery disease undergoing percutaneous coronary intervention (PCI) is based on a limited selection of clinical and imaging findings. Machine learning (ML) can consider a higher number and complexity of variables and may be useful for characterising cardiovascular risk, predicting outcomes, and identifying biomarkers in large population studies.