Identification of distinct clinical phenotypes of cardiogenic shock using machine learning consensus clustering approach

Identification of distinct clinical phenotypes of cardiogenic shock using machine learning consensus clustering approach

Abstract Background Cardiogenic shock (CS) is a complex state with many underlying causes and associated outcomes. It is still difficult to differentiate between various CS phenotypes. We investigated if the CS phenotypes with distinctive clinical profiles and prognoses might be found using the mach...

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Bibliographic Details
Main Authors: Li Wang, Yufeng Zhang, Renqi Yao, Kai Chen, Qiumeng Xu, Renhong Huang, Zhiguo Mao, Yue Yu
Format: Article
Language:English
Published: BMC 2023-08-01
Series:BMC Cardiovascular Disorders
Subjects:
Acute kidney injury
Artificial intelligence
Cardiogenic shock
Cluster
Intensive care unit
Machine learning
Online Access:https://doi.org/10.1186/s12872-023-03380-y

Internet

https://doi.org/10.1186/s12872-023-03380-y

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