Table 5 Predictive performances of different models in training set and validation set
From: Early predicting 30-day mortality in sepsis in MIMIC-III by an artificial neural networks model
Accuracy (95% CI) | Sensitivity (95% CI) | Specificity (95% CI) | AUC (95% CI) | |
|---|---|---|---|---|
Training set | ||||
ANN | 0.866 (0.838–0.894) | 0.850 (0.821–0.879) | 0.410 (0.370–0.450) | 0.873 (0.846–0.900) |
Logistic regression | 0.711 (0.674–0.748) | 0.662 (0.624–0.700) | 0.337 (0.299–0.375) | 0.720 (0.684–0.756) |
APACHEII | 0.615 (0.576–0.654) | 0.569 (0.529–0.609) | 0.367 (0.328–0.406) | 0.629 (0.607–0.651) |
SOFA | 0.574 (0.534–0.614) | 0.619 (0.580–0.658) | 0.413 (0.373–0.453) | 0.619 (0.596–0.641) |
P value | < 0.001 | < 0.001 | 0.029 | < 0.001 |
Validation set | ||||
ANN | 0.735 (0.714–0.756) | 0.624 (0.601–0.647) | 0.772 (0.752–0.792) | 0.811 (0.792–0.830) |
Logistic regression | 0.722 (0.701–0.743) | 0.604 (0.581–0.627) | 0.744 (0.723–0.765) | 0.752 (0.731–0.773) |
APACHEII | 0.401 (0.378–0.424) | 0.333 (0.311–0.355) | 0.841 (0.824–0.858) | 0.607 (0.584–0.630) |
SOFA | 0.609 (0.586–0.632) | 0.416 (0.392–0.440) | 0.788 (0.769–0.807) | 0.628 (0.605–0.651) |
P value | 0.272 | 0.197 | 0.095 | 0.002 |