Datasets:
annotations_creators:
- expert-generated
language:
- es
language_creators:
- expert-generated
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: MEDDOCAN
size_categories:
- 10K<n<100K
source_datasets:
- original
tags:
- clinical
- protected health information
- health records
task_categories:
- token-classification
task_ids:
- named-entity-recognition
Dataset Card for "meddocan"
Table of Contents
- Dataset Card for [Dataset Name]
Dataset Description
- Homepage: https://temu.bsc.es/meddocan/index.php/datasets/
- Repository: https://github.com/PlanTL-GOB-ES/SPACCC_MEDDOCAN
- Paper: http://ceur-ws.org/Vol-2421/MEDDOCAN_overview.pdf
- Leaderboard:
- Point of Contact:
Dataset Summary
A personal upload of the SPACC_MEDDOCAN corpus. The tokenization is made with the help of a custom spaCy pipeline.
Supported Tasks and Leaderboards
Name Entity Recognition
Languages
Dataset Structure
Data Instances
Data Fields
The data fields are the same among all splits.
Data Splits
| name | train | validation | test |
|---|---|---|---|
| meddocan | 10312 | 5268 | 5155 |
Dataset Creation
Curation Rationale
Source Data
Initial Data Collection and Normalization
Who are the source language producers?
Annotations
Annotation process
Who are the annotators?
Personal and Sensitive Information
Considerations for Using the Data
Social Impact of Dataset
Discussion of Biases
Other Known Limitations
Additional Information
Dataset Curators
Licensing Information
From the SPACCC_MEDDOCAN: Spanish Clinical Case Corpus - Medical Document Anonymization page:
This work is licensed under a Creative Commons Attribution 4.0 International License.
You are free to: Share — copy and redistribute the material in any medium or format Adapt — remix, transform, and build upon the material for any purpose, even commercially. Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.
For more information, please see https://creativecommons.org/licenses/by/4.0/
Citation Information
@inproceedings{Marimon2019AutomaticDO,
title={Automatic De-identification of Medical Texts in Spanish: the MEDDOCAN Track, Corpus, Guidelines, Methods and Evaluation of Results},
author={Montserrat Marimon and Aitor Gonzalez-Agirre and Ander Intxaurrondo and Heidy Rodriguez and Jose Lopez Martin and Marta Villegas and Martin Krallinger},
booktitle={IberLEF@SEPLN},
year={2019}
}
Contributions
Thanks to @GuiGel for adding this dataset.