A Novel AUC Maximization Imbalanced Learning Approach for Predicting Composite Outcomes in COVID-19 Hospitalized Patients

The COVID-19 patient data for composite outcome prediction often comes with class imbalance issues, i.e., only a small group of patients develop severe composite events after hospital admission, while the rest do not. An ideal COVID-19 composite outcome prediction model should possess strong imbalan...

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Veröffentlicht in:	IEEE journal of biomedical and health informatics Jg. 27; H. 8; S. 3794 - 3805
Hauptverfasser:	Wang, Guanjin, Kwok, Stephen Wai Hang, Yousufuddin, Mohammed, Sohel, Ferdous
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Schlagworte:	Area Under Curve AUC maximization learning Codes COVID-19 COVID-19 prediction Exact solutions Hospitalization Hospitals Humans imbalance learning Intubation Learning Machine Learning Maximization Objective function Optimization Patients Performance measurement Prediction models Predictive models Prognosis proximal support vector machines Support vector machines Training
ISSN:	2168-2194, 2168-2208, 2168-2208
Online-Zugang:	Volltext
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