Table 2.

AUC with 95% confidence interval and the accuracy rate of various methods in predicting 28-days mortality and compared with CNN plus SoftMax by Delong test.

Algorithms	AUC	SE	95%CI	Compared with CNN + SoftMax	Acc (%)
SIRS	0.59	0.0063	0.59–0.60	p < 0.0001	59.43
qSOFA	0.68	0.0061	0.67–0.69	p < 0.0001	67.27
RF	0.89	0.0032	0.89–0.89	p < 0.0001	62.56
KNN	0.84	0.0047	0.83–0.84	p < 0.0001	77.31
SVM	0.90	0.0031	0.89–0.90	p < 0.0001	74.33
SoftMax	0.88	0.0034	0.90–0.89	p < 0.0001	82.73
PCA + RF	0.89	0.0034	0.89–0.89	p < 0.0001	62.62
PCA + KNN	0.84	0.0050	0.84–0.85	p < 0.0001	81.67
PCA + SVM	0.89	0.0033	0.88–0.89	p < 0.0001	78.91
PCA + SoftMax	0.91	0.0031	0.90–0.91	p < 0.0001	83.48
AE + RF	0.84	0.0037	0.83–0.84	p < 0.0001	63.52
AE + KNN	0.81	0.0042	0.81–0.82	p < 0.0001	80.64
AE + SVM	0.89	0.0033	0.89–0.90	p < 0.0001	78.76
AE + SoftMax	0.90	0.0032	0.89–0.90	p < 0.0001	84.17
CNN + RF	0.90	0.0032	0.90–0.91	p < 0.0001	61.03
CNN + KNN	0.86	0.0040	0.85–0.86	p < 0.0001	81.73
CNN + SVM	0.92	0.0027	0.91–0.92	p < 0.0001	84.96
CNN + SoftMax	0.92	0.0027	0.92–0.92	None	87.01

Abbreviation: SIRS, systemic inflammatory response syndrome; qSOFA, quick sepsis-related organ failure assessment; RF, random forest; KNN, K nearest neighbor; SVM, support vector machine; PCA, principal component analysis; AE, autoencoder; CNN, convolutional neural network; AUC, area under the curve; SE standard error; CI, confidence interval; Acc, accuracy.