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ZENODO
Dataset . 2023
License: CC BY
Data sources: ZENODO
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
ZENODO
Dataset . 2023
License: CC BY
Data sources: Datacite
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MedProcNER Corpus: Gold Standard annotations for Clinical Procedures Information Extraction

Authors: Salvador Lima López; Eulàlia Farré Maduell; Luis Gascó Sánchez; Martin Krallinger;

MedProcNER Corpus: Gold Standard annotations for Clinical Procedures Information Extraction

Abstract

MedProcNER stands for MEDical PROCedure Named Entity Recognition. It is a shared task and set of resources focused on the detection, normalization and indexing of clinical procedures in medical documents in Spanish. MedProcNER is complementary to the DisTEMIST corpus (https://temu.bsc.es/distemist) as they both use the same document collection. Please cite if you use this dataset: Lima-López S, Farré-Maduell E, Gascó L, Nentidis A, Krithara A, Katsimpras G, Paliouras G, Krallinger M. Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023. Working Notes of CLEF. 2023. @article{lima2023overview, title={Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023}, author={Lima-L{\'o}pez, Salvador and Farr{\'e}-Maduell, Eul{\`a}lia and Gasc{\'o}, Luis and Nentidis, Anastasios and Krithara, Anastasia and Katsimpras, Georgios and Paliouras, Georgios and Krallinger, Martin}, journal={Working Notes of CLEF}, year={2023} } This repository includes the Train Set of the task, which includes a total of 750 documents, plus the annotated Test Set's 250 documents. A gazetteer of possible SNOMED CT codes for the normalization and indexing tasks is also part of the bundle as a lexical resource. In addition, a cross-mapping file of all SNOMED CT codes to MeSH is also included. Finally, we release an experimental multilingual Silver Standard version derived from the Spanish Gold Standard in 9 languages: English, Catalan, Italian, French, Portuguese, Romanian, Czech, Dutch and Swedish. These documents have been generated using an automatic annotation transfer process that works as follows: The text files were translated with a neural machine translation system. The annotations were translated with the same neural machine translation system. The translated annotations were transferred to the translated text files using a lexical approach and custom dictionaries. MedProcNER was developed by the Barcelona Supercomputing Center's NLP for Biomedical Information Analysis and used as part of BioASQ @ CLEF 2023. For more information on the corpus, annotation scheme and task in general, please visit: https://temu.bsc.es/medprocner. Resources: Web Citation: Lima-López S, Farré-Maduell E, Gascó L, Nentidis A, Krithara A, Katsimpras G, Paliouras G, Krallinger M. Overview of MedProcNER task on medical procedure detection and entity linking at BioASQ 2023. Working Notes of CLEF. 2023. Annotation guidelines Proceedings and participant papers Overview paper Overview talk slides at BioASQ/CLEF Additional resources and corpora If you are interested in MedProcNER, you might want to check out these corpora and resources: DisTEMIST (Corpus of disease mentions and normalization to SNOMED CT, same document collection) SympTEMIST (Corpus of symptoms, signs and findings mentions and normalization to SNOMED CT, same document collection) PharmaCoNER (Corpus of medications, drugs, chemical substances, genes, proteins and vaccine mentions and normalization, same document collection) MEDDOPROF (Corpus of mentions of professions, occupations and working status and normalization, different document collection with some overlapping documents) MEDDOPLACE (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, different document collection with some overlapping documents) MEDDOCAN (Corpus of mentions of place-related entity mentions, including departments, nationalities or patient movements etc.. and normalization, modified synthetic verions of the document collection) CANTEMIST (Corpus of cancer tumor morphology mentions and normalization, different document collection) CodiESp (Corpus of clinical case reportes with assigned clinical codes from ICD10, Spanish version, same document collection) LivingNER (Corpus of mentions of species, including human/family members, pathogens, food, etc.. and normalization to NCBI Taxonomy, different document collection with some overlapping documents) SPACCC-POS (Corpus of clinical case reports in Spanish annotated with POS-tags, same document collection) SPACCC-TOKEN (Corpus of clinical case reports in Spanish annotated with token-tags (word mention boundaries), same document collection) SPACCC-SPLIT (Corpus of clinical case reports in Spanish annotated with sentence boundary-tags, same document collection) MESINESP-2 (Corpus of manually indexed records with DeCS /MeSH terms comprising scientific literature abstracts, clinical trials, and patent abstracts, different document collection) License This work is licensed under a Creative Commons Attribution 4.0 International License. Contact If you have any questions or suggestions, please contact us at: - Salvador Lima-López ()- Martin Krallinger ()

Funded by the Plan de Impulso de las Tecnologías del Lenguaje (Plan TL).

Related Organizations
Keywords

procedure, normalization, ner, clinical nlp, entity linking, nlp, spanish, bioasq, bionlp, indexing

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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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