Table 2 Performance for mortality risk prediction of models in validation cohorts.

From: Machine learning based early warning system enables accurate mortality risk prediction for COVID-19

 

AUC (95% CI)

Accuracy (95% CI)

Sensitivity (95% CI)

Specificity (95% CI)

PPV (95% CI)

NPV (95% CI)

F1

Kappa

Brier

Internal validation cohort (SFV)

 MRPMC

0.9621 (0.9464–0.9778)

92.4% (90.1–94.4%)

57.3% (46.4–67.7%)

98.3% (96.8–99.2%)

85.0% (73.4–92.9%)

93.2% (90.8–95.2%)

0.685

0.644

0.051

 SVM

0.9594 (0.9424–0.9764)

92.4% (90.1–94.4%)

60.7% (49.8–70.9%)

97.8% (96.1–98.8%)

81.8% (70.4–90.2%)

93.7% (91.4–95.6%)

0.697

0.655

0.052

 GBDT

0.9454 (0.9246–0.9662)

91.5% (89.0–93.6%)

60.7% (49.8–70.9%)

96.6% (94.7–98.0%)

75.0% (63.4–84.5%)

93.6% (91.3–95.5%)

0.696

0.643

0.066

 LR

0.9614 (0.9456–0.9772)

92.1% (89.7–94.1%)

56.2% (45.3–66.7%)

98.1% (96.6–99.1%)

83.3% (71.5–91.7%)

93.1% (90.6–95.0%)

0.671

0.628

0.051

 NN

0.9615 (0.9456–0.9774)

92.1% (89.7–94.1%)

51.7% (40.8–62.4%)

98.9% (97.6–99.6%)

88.5% (76.6–95.7%)

92.5% (90.0–94.5%)

0.653

0.612

0.051

External validation cohort (OV)

 MRPMC

0.9760 (0.9613–0.9906)

95.5% (93.8–96.8%)

45.0% (32.1–58.4%)

99.6% (98.8–99.9%)

90.0% (73.5–97.9%)

95.7% (94.0–97.0%)

0.600

0.579

0.029

 SVM

0.9774 (0.9640–0.9908)

95.8% (94.1–97.0%)

50.0% (36.8–63.2%)

99.5% (98.6–99.9%)

88.2% (72.6–96.7%)

96.1% (94.5–97.4%)

0.638

0.618

0.028

 GBDT

0.9536 (0.9279–0.9793)

94.8% (93.0–96.2%)

48.3% (35.2–61.6%)

98.5% (97.4–99.3%)

72.5% (56.1–85.4%)

95.9% (94.3–97.2%)

0.580

0.553

0.039

 LR

0.9721 (0.9568–0.9875)

95.4% (93.7–96.7%)

45.0% (32.1–58.4%)

99.5% (98.6–99.9%)

87.1% (70.2–96.4%)

95.7% (94.0–97.0%)

0.593

0.572

0.031

 NN

0.9754 (0.9602–0.9906)

95.6% (94.0–96.9%)

46.7% (33.7–60.0%)

99.6% (98.8–99.9%)

90.3% (74.3–98.0%)

95.8% (94.2–97.1%)

0.615

0.595

0.028

External validation cohort (CHWH)

 MRPMC

0.9246 (0.8763–0.9729)

87.9% (80.6–93.2%)

42.1% (20.3–66.5%)

96.9% (91.2–99.4%)

72.7% (39.0–94.0%)

89.5% (82.0–94.7%)

0.533

0.470

0.083

 SVM

0.9067 (0.8482–0.9652)

88.8% (81.6–93.9%)

57.9% (33.5–79.8%)

94.6% (88.4–98.3%)

68.8% (41.3–89.0%)

92.0% (84.8–96.5%)

0.629

0.563

0.090

 GBDT

0.9021 (0.8347–0.9694)

87.9% (80.6–93.2%)

31.6% (12.6–56.6%)

99.0% (94.4–100.0%)

85.7% (42.1–99.6%)

88.1% (80.5–93.5%)

0.462

0.410

0.089

 LR

0.9213 (0.8710–0.9717)

87.1% (79.6–92.6%)

36.8% (16.3–61.6%)

96.9% (91.2–99.4%)

70.0% (34.8–93.3%)

88.7% (81.1–94.0%)

0.483

0.417

0.091

 NN

0.9202 (0.8700–0.9705)

88.8% (81.6–93.9%)

47.4% (24.5–71.1%)

96.9% (91.2–99.4%)

75.0% (42.8–94.5%)

90.4% (83.0–95.3%)

0.581

0.520

0.083

  1. SFV internal validation cohort of Sino-French New City Campus of Tongji Hospital, OV Optical Valley Campus of Tongji Hospital, CHWH The Central Hospital of Wuhan, MRPMC mortality risk prediction model for COVID-19, SVM support vector machine, GBDT gradient boosted decision tree, LR logistic regression, NN neural network, AUC area under the receiver operating characteristics curve, PPV positive predictive value, NPV negative predictive value, 95% CI 95% confidence interval.