Connectionists: CANTEMIST shared task: Cancer text mining, named entity recognition and clinical coding (IberLEF2020)

[Martin Krallinger]

**** Call for Participation Cantemist: CANcer TExt Mining Shared Task
(IberLEF - SEPLN 2020) ****



*Named Entity Recognition of Tumor Morphology Mentions and ICD-O-3 coding
track at SEPLN 2020*

https://temu.bsc.es/cantemist/



Plan TL Award for the Cantemist Track winners



Following the success of previous shared tasks we have coordinated in
collaboration with the BioCreative challenges (e.g. ChemDNER, ChemProt),
BioNLP-OST (PharmaCoNER), eHealth CLEF (CodiEsp) or IberLEF2019 (MEDDOCAN)
we are organizing the first shared task specifically focusing on named
entity recognition of a critical type of concept related to cancer,
namely *tumor
morphology*, called CANTEMIST. These previous efforts resulted in high
impact datasets, publications and new tools.



*The Cantemist sub-tracks:*

*1.CANTEMIST-NER*: finding mentions of tumor morphology in oncology cases.

*2.CANTEMIST-NORM:* recognition and mapping to concept identifiers from
ICD-O-3.

*3.CANTEMIST-CODING: *oncology clinical coding (multi-label classification)
assigning ICD-O-3 codes to clinical case documents.



*Key information*

1.     Cantemist web, info & detailed description:
https://temu.bsc.es/cantemist/

2.     Registration for Cantemist: https://temu.bsc.es/cantemist/?p=3956

3.     Datasets: https://zenodo.org/record/3878179



*Task motivation*

There is a pressing need to apply natural language processing (NLP) and
text mining technologies to process clinical texts in order to unlock
critical information that enables better clinical decision-making. NLP can
facilitate the use of information from literature and electronic health
records in biomedical data analysis. *Understanding diseases requires the
extraction of certain key entities like diseases, treatments or symptoms*
and their attributes from textual data, as has become clear from the recent
COVID-19 (SARS-CoV-2, coronavirus disease) pandemic, which showed the
current struggle in processing clinical documents written in various
languages.

With over *470 million* native speakers, there is a worldwide interest in
processing medical texts in Spanish (every 10 minutes, tens of thousands of
EHRs are produced just in Spain). Such technologies also have the potential
of being *adapted to handle other languages*, like Italian, German, French
or even English.

Results of systems capable of automatically processing clinical texts are
not only of interest for the medical user community or researchers working
on basic and applied health-related disciplines, but are also demanded by
the pharmaceutical industry and ultimately by patients.

Due to the special relevance of cancer as one of the leading causes of
death and the growing healthcare expenditures for oncological treatments a
specific classification resource for oncology has been constructed by the
WHO known as International Classification of Diseases for Oncology (*ICD-O*).
The CIE-O has been used for over 25 years as a standard resource to code
diagnosis of neoplasms in tumor and cancer registries as well as pathology
reports.



*Important dates*

June, 5: Train set and guidelines release

June, 12: First development set release

July, 3: Test and Background set release

Aug, 3: End of the evaluation period

Aug, 14: Paper submission

Sep 1: Camera-ready paper submission

Sep 23-25: SEPLN 2020 Conference



*Publications and workshop*

There will be an *evaluation workshop allocated at SEPLN 2020* where
participating teams can present their systems and results. Moreover,
participating teams will be invited to submit their system description
papers for publication at the *SEPLN 2020 Working Notes proceedings*. For
previous working notes see: http://ceur-ws.org/Vol-2421/



*Cantemist awards*

There will be three awards for the top-scoring teams promoted by the
Spanish Plan for the Advancement of Language Technology (Plan TL) and the
Barcelona Supercomputing Center (BSC).



*Main Track organizers*

●      *Martin Krallinger*, Barcelona Supercomputing Center, Spain

●      *Antonio Miranda*, Barcelona Supercomputing Center, Spain

●      *Eulália Farré*, Barcelona Supercomputing Center, Spain

●      *Jose Antonio*, Hospital 12 de Octubre, Madrid, Spain







*Scientific Committee*



·       Kirk Roberts, School of Biomedical Informatics, University of Texas
Health Science Center, USA

·       Parminder Bhatia, Amazon Health AI, USA

·       Irene Spasic, School of Computer Science & Informatics, co-Director
of the Data Innovation Research Institute, Cardiff University, UK

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

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

·       Hercules Dalianis, Department of Computer and Systems Sciences,
Stockholm University, Sweden

·       Kevin Bretonnel Cohen, Colorado School of Medicine, USA; LIMSI,
CNRS, Université Paris-Saclay, France

·       Karin Verspoor, School of Computing and Information Systems, Health
and Biomedical Informatics Centre, University of Melbourne







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Martin Krallinger
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Head of Biological Text Mining Unit
Barcelona Supercomputing Center (BSC-CNS)
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Oficina Técnica de sanidad del Plan TL
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