We identified the CADRADS scores in 1429 radiology reports using regular expressions for the CADRADS directly and for the degrees of stenosis. For the training we masked the direct CADRADS patterns.
Model card:
| Field | Value |
|---|---|
| Task | Multi-class sequence/text classification |
| Number of samples | 1,429 |
| Number of classes | 6 |
| Labels | 0, 1, 2, 3, 4, 5 |
| Base model | UMCU/CardioBERTa.nl_clinical |
| Architecture | RobertaForSequenceClassification |
| Classification head | Newly initialized before fine-tuning |
| Validation strategy | Stratified 10-fold cross-validation |
| Trained fold | Fold 0 only |
| Fold 0 train size | 1,286 |
| Fold 0 validation size | 143 |
| Class weighting | Yes |
Label distribution:
| Label | Count | Percentage |
|---|---|---|
| 0 | 345 | 24.1% |
| 1 | 281 | 19.7% |
| 2 | 333 | 23.3% |
| 3 | 317 | 22.2% |
| 4 | 135 | 9.4% |
| 5 | 18 | 1.3% |
Class weights:
| Label | Weight |
|---|---|
| 0 | 0.6914 |
| 1 | 0.8505 |
| 2 | 0.7168 |
| 3 | 0.7494 |
| 4 | 1.7568 |
| 5 | 12.6078 |
10-fold CV results.
"average_results": {
"avg_eval_accuracy": 0.863542795232936,
"std_eval_accuracy": 0.03399941533079611,
"avg_eval_f1": 0.8609888740211058,
"std_eval_f1": 0.034077598782922706,
"avg_eval_precision": 0.8642976260040041,
"std_eval_precision": 0.03264535733017625,
"avg_eval_recall": 0.863542795232936,
"std_eval_recall": 0.03399941533079611
}