| --- |
| license: gpl-3.0 |
| language: |
| - nl |
| base_model: |
| - UMCU/CardioDeBERTa.nl_clinical |
| pipeline_tag: text-classification |
| tags: |
| - cadrads |
| - healthcare |
| - radiology |
| --- |
| |
| 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. |
|
|
| ```json |
| "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 |
| } |
| ``` |
|
|
