Table 1.
Discrimination between women with and without postpartum hemorrhage using two machine learning and two statistical models
| Model. | Temporal Validation1 | Temporal and site validation2 | ||
|---|---|---|---|---|
| <2007 | >= 2007 | <2007 | >= 2007 | |
| Extreme Gradient Boosting | 0.95 (0.95 to 0.95) | 0.93 (0.92 to 0.93) | 0.93 (0.92 to 0.94) | 0.93 (0.92 to 0.94) |
| Random forest | 0.99 (0.99 to 1) | 0.92 (0.91 to 0.92) | 0.92 (0.91 to 0.92) | 0.92 (0.91 to 0.92) |
| Logistic regression with lasso regularization | 0.88 (0.87 to 0.88) | 0.87 (0.86 to 0.88) | 0.87 (0.86 to 0.88) | 0.87 (0.86 to 0.88) |
| Logistic regression model | 0.87 (0.87 to 0.88) | 0.87 (0.86 to 0.87) | 0.87 (0.86 to 0.87) | 0.87 (0.86 to 0.87) |
1
In temporal validation, models were constructed from the first phase (2002–2006) and externally validated (i.e., temporally) in the second phase (2007–2008).
2
In temporal and site validation, both clinical site-specific (total of 10 sites) and temporal validation were combined by using each site once as a validation sample, with the remaining sites used for model derivation during the first phase.
Data are presented as concordance index with 95% confidence intervals.