| | --- |
| | 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 |
| | --- |
| | |
| | # 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. |
