[CFP] MEDDOPROF Shared Task: Occupation detection and normalization in Spanish clinical documents

[Salvador Lima]

Taking advantage of the fact that the past May 1st was the International Workers' Day, we want to share the CFP of the MEDDOPROF shared task, focused on professions and employment statuses in health data.

MEDDOPROF Shared Task (IberLEF - SEPLN 2021): Medical Documents Profession Recognition shared task

https://temu.bsc.es/meddoprof/
MEDDOPROF Cup Awards by BSC-Plan TL [3,000€]

We are organizing the first shared task focusing on automatic recognition of professions and occupational status (and normalization to standard multilingual terminologies) in medical documents.

The relevance of text mining of professions and occupational status encompasses multiple human-interest areas, from health and social services, competitive intelligence, human resources, legal NLP and even gender studies.

The need to implement advanced NER systems to detect professions in medical texts has been underscored by the current pandemic, in which the risk of selected occupational groups has resulted in higher mortality and morbidity for these segments of the population. The relationships between disorders and professions may be explained by different factors like increased contact/exposure to hazardous substances, allergens or pathogens; physical injuries due to occupational accidents; higher degrees of social interaction of some professions, or even work-related conditions affecting mental health, just to name a few.

Additionally, targeted vaccination plans also benefit from better characterization of patient professions.

Following the success of previously organized shared tasks (i.e. Cantemist, PharmaCoNER, or Meddocan), we are now launching the MEDDOPROF shared task as part of the IberLEF 2021 evaluation initiative (co-located with SEPLN 2021), with the following sub-tracks:

* MEDDOPROF-NER: automatic detection of mentions of occupations (profession, employment status and activities).

* MEDDOPROF-CLASS: finding mentions of occupations and classifying them, whether they refer to the patients themselves, their family members or healthcare professionals.

* MEDDOPROF-NORM: mapping detected occupation mentions to their corresponding concept identifiers from standard multilingual occupation terminologies (ESCO and SNOMED-CT).

- Key information:

MEDDOPROF web: https://temu.bsc.es/meddoprof/

Data: https://doi.org/10.5281/zenodo.4694768

Annotation guidelines: https://doi.org/10.5281/zenodo.4694675

Registration: https://temu.bsc.es/meddoprof/registration

Google Group for updates: https://groups.google.com/g/meddoprof-shared-task

- Schedule

Test set release (start of evaluation period): June 1st, 2021

End of evaluation period (system submissions): June 7th, 2021

Working papers submission: June 21st, 2021

Notification of acceptance (peer-reviews): June 27th, 2021

Camera-ready system descriptions: July 4th, 2021

IberLEF @ SEPLN 2021: September 2021


- Publications and IBERLEF/SEPLN2021 workshop

Teams participating in MEDDOPROF will be invited to contribute a systems description paper for the IberLEF (SEPLN 2021) Working Notes proceedings, and a short presentation of their approach at the IberLEF 2021 workshop.

- Main Organizers

Martin Krallinger, Barcelona Supercomputing Center, Spain

Eulàlia Farré, Barcelona Supercomputing Center, Spain

Salvador Lima, Barcelona Supercomputing Center, Spain

Vicent Briva-Iglesias, D-REAL, Dublin City University, Ireland

Antonio Miranda-Escalada, Barcelona Supercomputing Center, Spain


- Scientific Committee

Sophia Ananadiou, Department of Computer Science, University of Manchester, UK

Josep Maria Haro Abad, Institut de Recerca Sant Joan de Déu

Goran Nenadic, Department of Computer Science, University of Manchester, UK

Aurélie Névéol, LIMSI-CNRS, Université Paris-Sud, France

Øystein Nytrø, Department of Computer and Information Science, Norges Teknisk-Naturvitenskapelige Universitet (NTNU)

Carlos Luis Parra Calderón, Head of Technological Innovation at Virgen del Rocío University Hospital, Institute of Biomedicine of Seville, Spain

Francisco Javier Sanz Valero, Escuela Nacional de Medicina del Trabajo, Instituto de Salud Carlos III, Spain

Ashish Tendulkar, Machine Learning Specialist at Google

Michelle Turner, Assistant Research Professor at Barcelona Institute for Global Health, Secretary-Treasurer International Society for Environmental Epidemiology (ISEE)

Ozlem Uzuner, George Mason University

Alfonso Valencia Herrera, Barcelona Supercomputing Center (BSC-CNS), Spain