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Periprocedural myocardial infarction (PMI) after percutaneous coronary intervention (PCI) is associated with the bad prognosis in patients. Current approaches to predict PMI fail to identify many people who would benefit from preventive treatment, and machine learning (ML) offers opportunity to improve the performance of ML models for PMI based on the big routine data.

A machine learning-based approach for the prediction of periprocedural myocardial infarction by using routine data