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

Yao Wang | Zendy; Kangjun Zhu | Zendy; Ya Li | Zendy; Qingbo Lv | Zendy; Guosheng Fu | Zendy; Wenbin Zhang | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

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

Author(s) -

Yao Wang,

Kangjun Zhu,

Ya Li,

Qingbo Lv,

Guosheng Fu,

Wenbin Zhang

Publication year - 2020

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-20-551

Subject(s) - medicine , myocardial infarction , machine learning , intensive care medicine , cardiology , computer science

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.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research