Machine Learning Approaches for Phenotyping in Cardiogenic Shock and Critical Illness-Reference-Cited by-同舟云学术

Machine Learning Approaches for Phenotyping in Cardiogenic Shock and Critical Illness

Published:2022-10 Issue:4 Volume:1 Page:100126
ISSN:2772-963X
Container-title:JACC: Advances
language:en
Short-container-title:JACC: Advances

Author:

Jentzer Jacob C.,Rayfield Corbin,Soussi Sabri,Berg David D.,Kennedy Jason N.,Sinha Shashank S.,Baran David A.^ORCID,Brant Emily,Mebazaa Alexandre,Billia Filio^ORCID,Kapur Navin K.,Henry Timothy D.,Lawler Patrick R.

Publisher

Elsevier BV

Reference64 articles.

1. Contemporary management of cardiogenic shock: a scientific statement from the American Heart Association;van Diepen;Circulation,2017

2. Variability in reporting of key outcome predictors in acute myocardial infarction cardiogenic shock trials;Tyler;Catheter Cardiovasc Interv,2022

3. Shock in the cardiac intensive care unit: changes in epidemiology and prognosis over time;Jentzer;Am Heart J,2021

4. Epidemiology of shock in contemporary cardiac intensive care units;Berg;Circ Cardiovasc Qual Outcomes,2019

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