descriptionPublicationkeyboard_double_arrow_right Article , Other literature type 01 Dec 2023 English Publisher:Elsevier BVJournal:Data in Brief, volume 51, page 109,720 (issn: 2352-3409,

Authors: Andrés Carvallo; Denis Parra; Hans Lobel; Gabriel Rada;

doi: 10.1016/j.dib.2023.109720 , 10.5281/zenodo.8179420 , 10.5281/zenodo.8339033 , 10.5281/zenodo.4959944

pmid: 37965606

pmc: PMC10641139

A comparative dataset: Bridging COVID-19 and other diseases through epistemonikos and CORD-19 evidence

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Abstract

This is the dataset for COVID-19 document type screening. It is composed of: - Epistemonikos train dataset - CORD-19 test dataset adapted for Evidence Based Medicine domain - XLNET model fine-tuned on Epistemonikos dataset. - BioBERT model fine-tuned on Epistemonikos dataset. Scripts to run experiments can be found at: https://github.com/afcarvallo/covid_19_document_type_screening

Related Organizations

Pontifical Catholic University of Chile
Chile

Keywords

user evaluation, Science (General), evidence based medicine, Natural language processing, Computer applications to medicine. Medical informatics, R858-859.7, neural language models, Biomedical text classification, Q1-390, Evidence based medicine, natural language processing, Covid-19, Data Article

3 Research products, page 1 of 1

Supplementary Material for the paper: COVID-19 document type screening with neural language models for evidence-based healthcare
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Supplementary Material: COVID-19 document type screening
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covid_19_document_type_screening software on GitHub
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