Prediction of acute kidney injury risk after cardiac surgery: using a hybrid machine learning algorithm

Background Acute kidney injury (AKI) is a serious complication after cardiac surgery. We derived and internally validated a Machine Learning preoperative model to predict cardiac surgery-associated AKI of any severity and compared its performance with parametric statistical models. Methods We conduc...

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Veröffentlicht in:	BMC medical informatics and decision making Jg. 22; H. 1; S. 137 - 10
Hauptverfasser:	Petrosyan, Yelena, Mesana, Thierry G., Sun, Louise Y.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 18.05.2022 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Acute Kidney Injury - diagnosis Acute Kidney Injury - epidemiology Acute Kidney Injury - etiology Acute renal failure Adult Algorithms Artificial intelligence Calibration Cardiac surgery-associated acute kidney injury Cardiac Surgical Procedures - adverse effects Cardiovascular disease Care and treatment Classification Computer applications Coronary artery bypass Data mining Datasets Diagnosis Feature selection Generalized linear models Goodness of fit Health aspects Health Informatics Health risk assessment Health risks Heart Heart surgery Humans Information Systems and Communication Service Kidneys Learning algorithms Machine Learning Management of Computing and Information Systems Mathematical models Medicine Medicine & Public Health Methods Model accuracy Mortality Patient outcomes Patients Predictive modeling Random Forests Regression models Retrospective Studies Risk Risk Assessment Risk Factors Risk groups Statistical analysis Statistical methods Statistical models Statistical tests Surgery Canada United States > US Cardiac surgery-associated acute kidney injury Random Forests Predictive modeling Data mining Machine Learning
ISSN:	1472-6947, 1472-6947
Online-Zugang:	Volltext
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