
# 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.
task_categories:text-classification annotations_creators:expert-generated language_creators:expert-generated multilinguality:monolingual size_categories:1K<n<10K source_datasets:original language:es license:afl-3.0 radiology biomedicine ICD-10 region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["expert-generated"], "language": ["es"], "license": ["afl-3.0"], "multilinguality": ["monolingual"], "size_categories": ["1K<n<10K"], "source_datasets": ["original"], "task_categories": ["text-classification"], "pretty_name": "CARES", "tags": ["radiology", "biomedicine", "ICD-10"], "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}}
TAGS
#task_categories-text-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Spanish #license-afl-3.0 #radiology #biomedicine #ICD-10 #region-us 


# 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.
# 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.
TAGS
#task_categories-text-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Spanish #license-afl-3.0 #radiology #biomedicine #ICD-10 #region-us 
 # 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.
passage: TAGS
#task_categories-text-classification #annotations_creators-expert-generated #language_creators-expert-generated #multilinguality-monolingual #size_categories-1K<n<10K #source_datasets-original #language-Spanish #license-afl-3.0 #radiology #biomedicine #ICD-10 #region-us 
# 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.