Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention

Machine learning to predict no reflow and in-hospital mortality in patients with ST-segment elevation myocardial infarction that underwent primary percutaneous coronary intervention

Background: The machine learning algorithm (MLA) was implemented to establish an optimal model to predict the no reflow (NR) process and in-hospital death that occurred in ST-elevation myocardial infarction (STEMI) patients who underwent primary percutaneous coronary intervention (pPCI). Methods: Th...

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Bibliographic Details
Main Authors: Deng, L. (Author), Huang, D. (Author), Su, X. (Author), Zeng, X. (Author), Zhao, X. (Author), Zhou, M. (Author)
Format: Article
Language:English
Published: BioMed Central Ltd 2022
Subjects:
coronary angiography
Coronary Angiography
hospital mortality
Hospital Mortality
human
Humans
In-hospital mortality
machine learning
Machine learning
Machine Learning
no reflow phenomenon
No-reflow
No-Reflow Phenomenon
percutaneous coronary intervention
Percutaneous Coronary Intervention
Primary percutaneous coronary intervention
procedures
Retrospective Studies
retrospective study
ST Elevation Myocardial Infarction
ST segment elevation myocardial infarction
ST-elevation myocardial infarction
Online Access:View Fulltext in Publisher

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