Efficacy prediction of noninvasive ventilation failure based on the stacking ensemble algorithm and autoencoder

Background Early prediction of noninvasive ventilation failure is of great significance for critically ill ICU patients to escalate or change treatment. Because clinically collected data are highly time-series correlated and have imbalanced classes, it is difficult to accurately predict the efficacy...

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Vydáno v:	BMC medical informatics and decision making Ročník 22; číslo 1; s. 27 - 15
Hlavní autoři:	Liang, Na, Wang, Chengliang, Duan, Jun, Xie, Xin, Wang, Yu
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	London BioMed Central 31.01.2022 BioMed Central Ltd Springer Nature B.V BMC
Témata:	Accuracy Algorithms Analysis Autoencoder Care and treatment Classification Critically ill Data collection Data structures Datasets Decision making Decision trees Efficacy prediction Failure Feature extraction Health aspects Health Informatics Hospitals Information Systems and Communication Service Intubation Long short-term memory Machine learning Management of Computing and Information Systems Medical research Medicine Medicine & Public Health Mortality Noninvasive ventilation Oxygenation Patients Physicians Predictions Sepsis SMSN model Stacking Statistical analysis Time series Ventilation Ventilators China United States > US Noninvasive ventilation SMSN model Autoencoder Efficacy prediction
ISSN:	1472-6947, 1472-6947
On-line přístup:	Získat plný text
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