Table 2.
Predictive performance comparison according to input features.
| CDM-based clinical covariate | CDM-based clinical covariate + radiology/EMG report text model | CDM-based clinical covariate + radiology/EMG report text + EMG grade model | |
|---|---|---|---|
| LASSO LR | |||
| AUROC | 0.746 [0.685–0.801] | 0.768 [0.715–0.814] | 0.772 [0.723–0.821] |
| AUPRC | 0.432 [0.341–0.532] | 0.465 [0.357–0.498] | 0.471 [0.366–0.499] |
| Accuracy | 0.782 | 0.801 | 0.808 |
| F1-score | 0.495 | 0.523 | 0.531 |
| RF | |||
| AUROC | 0.750 [0.690–0.809] | 0.779 [0.732–0.821] | 0.785 [0.727–0.829] |
| AUPRC | 0.441 [0.346–0.535] | 0.478 [0.364–0.491] | 0.485 [0.387–0.520] |
| Accuracy | 0.786 | 0.809 | 0.815 |
| F1-score | 0.501 | 0.536 | 0.545 |
| GBM | |||
| AUROC | 0.759 [0.711–0.815] | 0.789 [0.738–0.826] | 0.792 [0.732–0.830] |
| AUPRC | 0.459 [0.363–0.541] | 0.492 [0.384–0.503] | 0.499 [0.382–0.531] |
| Accuracy | 0.794 | 0.818 | 0.823 |
| F1-score | 0.519 | 0.552 | 0.561 |
LASSO LR least absolute shrinkage and selection operator logistic regression, RF random forest, GBM gradient boosting machine, CDM common data model, EMG electromyography.