Connectionists: [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
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://mailman.srv.cs.cmu.edu/pipermail/connectionists/attachments/20210503/e327fd72/attachment.html>