Since the COVID-19 outbreak, many hospitals suffered from a surge of some high-risk inpatients needing to be admitted to the ICU. In this study, we propose a method
predicting the likelihood of COVID-19 inpatients’ admission to the ICU within a time frame of 12 hours. Four steps, the Bayesian Ridge Regression-based missing value imputation, the synthesis of training samples by the combination of two rows (the first and another row) of each patient, customized oversampling, and XGBoost classifier, are used for the proposed method. In the experiment, the AUC-ROC and F-score of our method is compared with those of other methods using various imputation techniques and classifiers. Our method achieves the best performance among the methods.
Lee, Yoon Sang and Sikora, Riyaz T.
"Short-term prediction of ICU admission for COVID-19 inpatients,"
Journal of International Technology and Information Management: Vol. 31:
3, Article 2.
Available at: https://scholarworks.lib.csusb.edu/jitim/vol31/iss3/2