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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Vydané v:	IEEE journal of biomedical and health informatics Ročník 27; číslo 8; s. 3794 - 3805
Hlavní autori:	Wang, Guanjin, Kwok, Stephen Wai Hang, Yousufuddin, Mohammed, Sohel, Ferdous
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	United States IEEE 01.08.2023 The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Predmet:	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
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