Urine output as one of the most important features in differentiating in-hospital death among patients receiving extracorporeal membrane oxygenation: a random forest approach

Urine output as one of the most important features in differentiating in-hospital death among patients receiving extracorporeal membrane oxygenation: a random forest approach

Abstract Background It is common to support cardiovascular function in critically ill patients with extracorporeal membrane oxygenation (ECMO). The purpose of this study was to identify patients receiving ECMO with a considerable risk of dying in hospital using machine learning algorithms. Methods A...

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Bibliographic Details
Main Authors: Sheng-Nan Chang, Nian-Ze Hu, Jo-Hsuan Wu, Hsun-Mao Cheng, James L. Caffrey, Hsi-Yu Yu, Yih-Sharng Chen, Jiun Hsu, Jou-Wei Lin
Format: Article
Language:English
Published: BMC 2023-09-01
Series:European Journal of Medical Research
Subjects:
Extracorporeal membrane oxygenation
Machine learning algorithm
Random forest
Oliguria
Online Access:https://doi.org/10.1186/s40001-023-01294-1

Internet

https://doi.org/10.1186/s40001-023-01294-1

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