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MEDDOPROF corpus: complete gold standard annotations for occupation detection in medical documents in Spanish

Authors: Lima-López, Salvador; Eulàlia Farré-Maduell; Miranda-Escalada, Antonio; Briva-Iglesias, Vicent; Krallinger, Martin;

MEDDOPROF corpus: complete gold standard annotations for occupation detection in medical documents in Spanish

Abstract

UPDATE 27/09/2022: A complete normalization of all mentions in the corpus to SNOMED CT has been added to the 'meddoprof-norm.tsv' file. Description This repository contains the complete MEDDOPROF Gold Standard, a collection of 1,844 clinical cases in Spanish with annotations for occupations, working statuses and activities. MEDDOPROF is a Shared Task celebrated in 2021 that explores the application of natural language processing to occupational health. If you'd like to learn more, please visit: https://temu.bsc.es/meddoprof. Folder and File Structure The corpus' files are presented in the format used by the annotation tool brat. That is, for each clinical case there is a .txt file with the text and a .ann file with its corresponding annotations. - meddoprof-ner/ Clinical cases annotated with these labels: PROFESION (PROFESSION), SITUACION_LABORAL (WORKING_STATUS) or ACTIVIDAD (ACTIVIDAD). - meddoprof-class/ Clinical cases with the same annotations as 'meddoprof-ner' but with these labels instead: PACIENTE (patient), FAMILIAR (family member), SANITARIO (health professional) or OTRO (other). - ner_class_joint/ Clinical cases with both levels of annotation (ner and class) joint (that is, a mention classified as as PROFESOR in meddoprof-ner and as PACIENTE in meddoprof-class would be PROFESION-PACIENTE here). - meddoprof-norm.tsv Tab-separated file (.tsv) with the mapping of each mention in the corpus to ESCO and SNOMED CT. The file has five columns: filename, mention text, span, ESCO code and SNOMED code. Additionally, two files with the filenames of the train and test partitions are included. Please cite if you use this resource: Salvador Lima-López, Eulàlia Farré-Maduell, Antonio Miranda-Escalada, Vicent Brivá-Iglesias and Martin Krallinger. NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts. In Procesamiento del Lenguaje Natural, 67. 2021. @article{meddoprof, title={NLP applied to occupational health: MEDDOPROF shared task at IberLEF 2021 on automatic recognition, classification and normalization of professions and occupations from medical texts}, author={Lima-López, Salvador and Farré-Maduell, Eulàlia and Miranda-Escalada, Antonio and Brivá-Iglesias, Vicent and Krallinger, Martin}, journal = {Procesamiento del Lenguaje Natural}, volume = {67}, year={2021}, issn = {1989-7553}, url = {http://journal.sepln.org/sepln/ojs/ojs/index.php/pln/article/view/6393}, pages = {243--256} } Related Resources: - Web - Training Data - Test set - Codes Reference List (for MEDDOPROF-NORM) - Annotation Guidelines - Occupations Gazetteer MEDDOPROF is part of the IberLEF 2021 workshop, which is co-located with the SEPLN 2021 conference. For further information, please visit https://temu.bsc.es/meddoprof/ or email us at encargo-pln-life@bsc.es MEDDOPROF is promoted by the Plan de Impulso de las Tecnologías del Lenguaje de la Agenda Digital (Plan TL) and the Spanish government's 2020 Proyectos de I+D+i RTI Tipo A (AI4PROFHEALTH - DESCIFRANDO EL PAPEL DE LAS PROFESIONES EN LA SALUD DE LOS PACIENTES A TRAVES DE LA MINERIA DE TEXTOS (PID2020-119266RA-I00)).

Related Organizations
Keywords

occupations, gold standard, corpus, entity linking, nlp, normalization, entity grounding, ner, shared task, employment status, clinical nlp, professions, bionlp

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
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