Table 2:

The performance of different approaches in predicting risk of mortality for patients with COVID-19.

Model	Observ. window	AUROC (95% CI)	AUPRC (95% CI)	Model	Observ. window	AUROC (95% CI)	AUPRC (95% CI)
LR	8	0.699 (0.687, 0.713)	0.475 (0.447, 0.504)	LR	24	0.732 (0.721, 0.744)	0.524 (0.497, 0.552)
RF	8	0.704 (0.693, 0.715)	0.480 (0.453, 0.501)	RF	24	0.778 (0.768, 0.787)	0.593 (0.573, 0.610)
SVM	8	0.700 (0.692, 0.708)	0.523 (0.511, 0.536)	SVM	24	0.760 (0.743, 0.778)	0.560 (0.541, 0.583)
XG-boost	8	0.675 (0.658, 0.690)	0.460 (0.442, 0.478)	XG-boost	24	0.758 (0.746, 0.769)	0.563 (0.540, 0.587)
LSTM	8	0.685 (0.670, 0.703)	0.489 (0.465, 0.515)	LSTM	24	0.766 (0.751, 0.771)	0.524 (0.503, 0.549)
SimCLR	8	0.754 (0.743, 0.766)	0.554 (0.529, 0.581)	SimCLR	24	0.806 (0.796, 0.814)	0.620 (0.599, 0.643)
SupCLR	8	0.758 (0.740, 0.768)	0.571 (0.553, 0.589)	SupCLR	24	0.809 (0.799, 0.818)	0.627 (0.610, 0.646)
EKPS-CL	8	0.771 (0.756, 0.786)	0.615 (0.598, 0.632)	EKPS-CL	24	0.827 (0.815, 0.839)	0.659 (0.647, 0.672)
LR	12	0.701 (0.687, 0.716)	0.471 (0.452, 0.491)	LR	48	0.796 (0.787, 0.806)	0.604 (0.581, 0.628)
RF	12	0.725 (0.714, 0.736)	0.544 (0.523, 0.563)	RF	48	0.805 (0.797, 0.812)	0.613 (0.593, 0.623)
SVM	12	0.691 (0.685, 0.698)	0.504 (0.489, 0.518)	SVM	48	0.787 (0.774, 0.799)	0.610 (0.594, 0.627)
XG-boost	12	0.683 (0.668, 0.699)	0.486 (0.463, 0.509)	XG-boost	48	0.801 (0.791, 0.810)	0.619 (0.598, 0.640)
LSTM	12	0.699 (0.693, 0.706)	0.502 (0.494, 0.511)	LSTM	48	0.806 (0.794, 0.813)	0.636 (0.616, 0.657)
SimCLR	12	0.772 (0.760, 0.782)	0.565 (0.541, 0.584)	SimCLR	48	0.853 (0.843, 0.864)	0.700 (0.689, 0.713)
SupCLR	12	0.783 (0.771, 0.794)	0.590 (0.561, 0.617)	SupCLR	48	0.869 (0.859, 0.879)	0.707 (0.690, 0.726)
EKPS-CL	12	0.784 (0.771, 0.798)	0.613 (0.589, 0.637)	EKPS-CL	48	0.872 (0.861, 0.882)	0.731 (0.711, 0.748)