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

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 total of...

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Veröffentlicht in:	European journal of medical research Jg. 28; H. 1; S. 1 - 11
Hauptverfasser:	Chang, Sheng-Nan, Hu, Nian-Ze, Wu, Jo-Hsuan, Cheng, Hsun-Mao, Caffrey, James L., Yu, Hsi-Yu, Chen, Yih-Sharng, Hsu, Jiun, Lin, Jou-Wei
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 15.09.2023 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Algorithms Analysis Biomedicine Cardiopulmonary resuscitation CPR Data mining Extracorporeal membrane oxygenation Health aspects Infectious Diseases Internal Medicine Machine learning Machine learning algorithm Medical prognosis Medical research Medicine Medicine & Public Health Medicine, Experimental Missing data Mortality Oliguria Oncology Patients Random forest Regression analysis Surgery Survival analysis Taiwan Urine Variables Taiwan United States > US Extracorporeal membrane oxygenation Random forest Oliguria Machine learning algorithm
ISSN:	2047-783X, 0949-2321, 2047-783X
Online-Zugang:	Volltext
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