Connectionists: 1st CFP: MEDDOPLACE task on medical location extraction, normalization & toponym resolution (IberlEF/SEPLN 2023)

[Salvador Lima]

(Apologies for cross-posting!)
1st CFP MEDDOPLACE Shared Task @ IberLEF/SEPLN2023 [Medical Documents
PLAce-related Content Extraction]

Info:

   -

   Web: https://temu.bsc.es/meddoplace/
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   Registration: https://temu.bsc.es/meddoplace/registration
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   Data: https://zenodo.org/record/7707567
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   Guidelines: https://zenodo.org/record/7775235

A. INTRODUCTION:

Location information represents one of the most relevant types of entities
for high impact practical NLP solutions, resulting in a variety of
applications adapted to different languages, content types and text genres.

Despite these previous efforts, the use, application, and exploitation of
location-related entity detection (including sociodemographic information
as well as more domain-specific things like clinical departments) from
medical content was not sufficiently addressed. The performance of general
purpose location NER systems applied on clinical texts is still poor,
usually covering only general geolocation mentions, lacking sufficient
granularity and not taking into account appropriate normalization or
linking of the extracted locations to widely used geocoding resources,
terminologies or vocabularies (like PlusCodes, GeoNames, or SNOMED CT
concepts), thus hindering the practical exploitation of the generated
results.

To address these issues we organize the MEDDOPLACE shared task (part of the
IberLEF/SEPLN2023 initiative) devoted to the recognition, normalization and
classification of location and location-related concept mentions for high
impact healthcare data mining scenarios.

For this task we will release the MEDDOPLACE corpus, a large collection of
clinical case reports written in Spanish that were exhaustively annotated
manually by linguists and medical experts to label location-relevant entity
mentions, following detailed annotation guidelines and entity linking
procedures.

The practical implications of this task include:

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   Patient management: The detection of locations, origin of patients,
   their language is relevant for healthcare safety, management, patient
   communication and appropriate treatment options.
   -

   Diagnosis & prognosis: Location information is important for the
   diagnosis or prognosis of some diseases that are more endemic to certain
   regions or particular geographical environments.
   -

   Health risk factors: Geolocation information can be a risk factor in
   case of exposure to radiation, work-related or environmental contaminants
   affecting patients health.
   -

   Mobility: Due to the increasing mobility of populations, detection of
   patients' travels and movements can improve early detection and tracing of
   infectious disease outbreaks, and thus enable taking preventive
   measurements.
   -

   Traceability: the detection of medical departments, facilities and
   services is critical to support the traceability of the patient’s route
   through the health services.

The expected results as well as provided resources for this task show a
clear multilingual adaptation potential and could have an impact beyond
healthcare documents, being relevant for processing tourism-related content
(traveling) or even legal texts.

B. TASKS DESCRIPTION:

The MEDDOPLACE task is structured into three subtracks:

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   MEDDOPLACE-NER: Given a collection of plain text documents, systems have
   to return the exact character offsets of all location and location-related
   mentions.
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   MEDDOPLACE-NORM: Given a collection of entities and their origin in
   text, systems have to normalize them to their corresponding GeoNames
   (Toponym Resolution), PlusCodes (POIs Toponym Resolution) and SNOMED CT
   (Entity Linking) concept, depending on entity type.
   -

   MEDDOPLACE-CLASS: Classification of detected location entities into four
   subcategories of clinical relevance (patient’s origin place; residence’s
   location; place where the patient has traveled to/from; place where the
   patient has received medical attention)

Publications and IBERLEF/SEPLN2023 workshop

Teams participating in MEDDOPLACE will be invited to contribute a systems
description paper for the IberLEF (SEPLN 2023) Working Notes proceedings,
and a short presentation of their approach at the IberLEF 2023 workshop.

Tentative Schedule:

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   Train set: March 27th, 2023
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   Test set release (start of evaluation period): April 3rd, 2023
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   End of evaluation period (system submissions): May 10th, 2023
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   Working papers submission: June 5th, 2023
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   Notification of acceptance (peer-reviews): June 23rd, 2023
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   Camera-ready system descriptions: July 6th, 2023
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   IberLEF @ SEPLN 2023: September 27th-29th, 2023

Organizers:

MEDDOPLACE is organized by the Barcelona Supercomputing Center’s NLP for
Biomedical Information Analysis, as well as some external collaborators:

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   Martin Krallinger, Barcelona Supercomputing Center, Spain
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   Salvador Lima, Barcelona Supercomputing Center, Spain
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   Eulàlia Farré, Barcelona Supercomputing Center, Spain
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   Luis Gascó, Barcelona Supercomputing Center, Spain
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   Vicent Briva-Iglesias, D-REAL, Dublin City University, Ireland


-- 
Salvador Lima Lopez
RESEARCH ENGINEER
Life Sciences - NLP for Biomedical Information Analysis, BSC-CNS
Barcelona, Spain
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