Machine learning for the prediction of acute kidney injury in patients with sepsis

Background Acute kidney injury (AKI) is the most common and serious complication of sepsis, accompanied by high mortality and disease burden. The early prediction of AKI is critical for timely intervention and ultimately improves prognosis. This study aims to establish and validate predictive models...

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Veröffentlicht in:	Journal of translational medicine Jg. 20; H. 1; S. 215 - 12
Hauptverfasser:	Yue, Suru, Li, Shasha, Huang, Xueying, Liu, Jie, Hou, Xuefei, Zhao, Yumei, Niu, Dongdong, Wang, Yufeng, Tan, Wenkai, Wu, Jiayuan
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London BioMed Central 13.05.2022 BioMed Central Ltd Springer Nature B.V BMC
Schlagworte:	Acute kidney injury Acute renal failure Algorithms Biomedical and Life Sciences Biomedicine Blood pressure Complications and side effects Computational Modelling and Epidemiology Datasets Disease Feature selection Health aspects Intensive care Kidneys Laboratories Learning algorithms Machine learning Medical prognosis Medicine/Public Health MIMIC- III database Mortality Neural networks Patients Physiology Prediction model Prediction models Risk factors Risk groups Sepsis Support vector machines Vital signs China Sepsis MIMIC- III database Acute kidney injury Machine learning Prediction model
ISSN:	1479-5876, 1479-5876
Online-Zugang:	Volltext
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