|
|
--- |
|
|
annotations_creators: |
|
|
- expert-generated |
|
|
language: |
|
|
- es |
|
|
language_creators: |
|
|
- expert-generated |
|
|
license: cc-by-nc-sa-4.0 |
|
|
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. |
|
|
|
|
|
# Citing |
|
|
If you use the lexicon in your research, please cite: [CARES: A Corpus for classification of Spanish Radiological reports](https://doi.org/10.1016/j.compbiomed.2023.106581). |
|
|
|
|
|
``` |
|
|
@article{chizhikova2023, |
|
|
title = {CARES: A Corpus for classification of Spanish Radiological reports}, |
|
|
journal = {Computers in Biology and Medicine}, |
|
|
volume = {154}, |
|
|
pages = {106581}, |
|
|
year = {2023}, |
|
|
issn = {0010-4825}, |
|
|
doi = {https://doi.org/10.1016/j.compbiomed.2023.106581}, |
|
|
url = {https://www.sciencedirect.com/science/article/pii/S001048252300046X}, |
|
|
author = {Mariia Chizhikova and Pilar López-Úbeda and Jaime Collado-Montañez and Teodoro Martín-Noguerol and Manuel C. Díaz-Galiano and Antonio Luna and L. Alfonso Ureña-López and M. Teresa Martín-Valdivia}, |
|
|
} |
|
|
``` |
