ClinAIS Corpus: Automatic Identification of Sections in Spanish Clinical Documents - IberLEF 2023

Introduction

The ClinAIS task presented at IberLEF 2023 aims to tackle the problem of automatic identification of sections in unstructured Spanish clinical documents. The task is focused on identifying 7 predefined medical sections: Present Illness, Derived from/to, Past Medical History, Family history, Exploration, Treatment, and Evolution in ECNs, mainly progress notes from the CodiEsp 2020 dataset [1].

Electronic Clinical Narratives (ECN) have become the standard for storing all the information a practitioner finds relevant to describe and evaluate a patient's clinical episode or evolution. These documents contain descriptions of previous pathologies, undergone procedures, evolution of a given disease, or prescribed treatments. Secondary use of ECN tackles diverse tasks, including identifying rare medical events, predicting hospital re-admissions, or in Public Health Surveillance among others.

Identifying medical sections in the patient narratives documented in ECNs is a crucial task for higher-level applications. Section identification consists of dividing the text into semantic segments categorized with a set of predefined labels and, provides new insights about entities, which might be completely different depending on the section in which they occur.

More Information

Visit the official ClinAIS task webpage for more information about the task:

Task & Data Information

Specific details about the evaluation.

Important dates.

Dataset License

The presented dataset is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Contact

Please contact us if you have any questions or concerns at [e-mail] and feel free to make use of the CodaLab forum.

Citations

Please cite both articles if you use this dataset.

Dataset and Evaluation Metric

I. de la Iglesia, M. Vivó, P. Chocrón, G. de Maeztu, K. Gojenola, A. Atutxa, An Open Source Corpus and Automatic Tool for Section Identification in Spanish Health Records, Journal of Biomedical Informatics 145 (2023) 104461. URL: https://www.sciencedirect.com/science/article/pii/S153204642300182X. doi:https://doi.org/10.1016/j.jbi.2023.104461.

@article{delaiglesia2023104461,
author = {Iker de la Iglesia and Maria Viv{\'{o}} and Paula Chocr{\'{o}}n and Gabriel de Maeztu and Koldo Gojenola and Aitziber Atutxa},
title = {{A}n {O}pen {S}ource {C}orpus and {A}utomatic {T}ool for {S}ection {I}dentification in {S}panish {H}ealth {R}ecords},
journal = {Journal of Biomedical Informatics},
volume = {145},
pages = {104461},
year = {2023},
issn = {1532-0464},
doi = {https://doi.org/10.1016/j.jbi.2023.104461},
url = {https://www.sciencedirect.com/science/article/pii/S153204642300182X}
}

Task Overview

I. de la Iglesia, M. Vivó, P. Chocrón, G. de Maeztu, K. Gojenola, A. Atutxa, Overview of ClinAIS at IberLEF 2023: Automatic Identification of Sections in Clinical Documents in Spanish, Procesamiento del Lenguaje Natural 71 (2023) 289–299. URL: http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6560.

@article{PLN6560,
author = {Iker de la Iglesia and Maria Viv{\'{o}} and Paula Chocr{\'{o}}n and Gabriel de Maeztu and Koldo Gojenola and Aitziber Atutxa},
title = {{Overview of ClinAIS at IberLEF 2023: Automatic Identification of Sections in Clinical Documents in Spanish}},
journal = {{Procesamiento del Lenguaje Natural}},
volume = {71},
year = {2023},
issn = {1989-7553},
url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6560},
pages = {289--299}
}

Acknowledgments

This work has been partially supported by the HiTZ Center and the Basque Government, Spain (Research group funding IT1570-22) as well as by the Spanish Ministry of Universities, Science and Innovation MCIN/AEI/10.13039/501100011033 by means of the projects:

DOTT-HEALTH/PAT-MED PID2019-543106942RB-C31

EDHER-MED/EDHIA PID2022-136522OB-C22

EU NextGeneration EU/PRTR projects:

ANTIDOTE PCI2020-120717-2 EU ERA-Net CHIST-ERA

DeepR3 TED2021-130295B-C31