Development of a machine learning model to predict the risk of late cardiogenic shock in patients with ST-segment elevation myocardial infarction

BACKGROUND: The in-hospital mortality of patients with ST-segment elevation myocardial infarction (STEMI) increases to more than 50% following a cardiogenic shock (CS) event. This study highlights the need to consider the risk of delayed calculation in developing in-hospital CS risk models. This rep...

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Detalles Bibliográficos
Autores principales: Bai, Zhixun, Hu, Shan, Wang, Yan, Deng, Wenwen, Gu, Ning, Zhao, Ranzun, Zhang, Wei, Ma, Yi, Wang, Zhenglong, Liu, Zhijiang, Shen, Changyin, Shi, Bei
Formato: Online Artículo Texto
Lenguaje:English
Publicado: AME Publishing Company 2021
Materias:
Original Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8350690/
https://www.ncbi.nlm.nih.gov/pubmed/34430603
http://dx.doi.org/10.21037/atm-21-2905

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8350690/
https://www.ncbi.nlm.nih.gov/pubmed/34430603
http://dx.doi.org/10.21037/atm-21-2905

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