Datasets:
| annotations_creators: | |
| - expert-generated | |
| language: | |
| - es | |
| language_creators: | |
| - expert-generated | |
| license: | |
| - afl-3.0 | |
| multilinguality: | |
| - monolingual | |
| pretty_name: CARES | |
| size_categories: | |
| - 1K<n<10K | |
| source_datasets: | |
| - original | |
| tags: | |
| - radiology | |
| - biomedicine | |
| - ICD-10 | |
| task_categories: | |
| - text-classification | |
| dataset_info: | |
| features: | |
| - name: iddoc | |
| dtype: float64 | |
| - name: id | |
| dtype: int64 | |
| - name: full_text | |
| dtype: string | |
| - name: icd10 | |
| sequence: string | |
| - name: general | |
| sequence: string | |
| - name: chapters | |
| sequence: int64 | |
| - name: area | |
| sequence: string | |
| splits: | |
| - name: train | |
| num_bytes: 3377631 | |
| num_examples: 2253 | |
| - name: test | |
| num_bytes: 1426962 | |
| num_examples: 966 | |
| download_size: 2291080 | |
| dataset_size: 4804593 | |
| # CARES - A Corpus of Anonymised Radiological Evidences in Spanish 📑🏥 | |
| CARES is a high-quality text resource manually labeled with ICD-10 codes and reviewed by radiologists. These types of resources are essential for developing automatic text classification tools as they are necessary for training and fine-tuning our computational systems. | |
| The CARES corpus has been manually annotated using the ICD-10 ontology, which stands for for the 10th version of the International Classification of Diseases. For each radiological report, a minimum of one code and a maximum of 9 codes were assigned, while the average number of codes per text is 2.15 with the standard deviation of 1.12. | |
| The corpus was additionally preprocessed in order to make its format coherent with the automatic text classification task. Considering the hierarchical structure of the ICD-10 ontology, each sub-code was mapped to its respective code and chapter, obtaining two new sets of labels for each report. The entire CARES collection contains 6,907 sub-code annotations among the 3,219 radiologic reports. There are 223 unique ICD-10 sub-codes within the annotations, which were mapped to 156 unique ICD-10 codes and 16 unique chapters of the cited ontology. | |
| As for the dataset train and test subsets, a stratified split was performed in order to guarantee that the number of labels in the test data is representative. |