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A comparative dataset: Bridging COVID-19 and other diseases through epistemonikos and CORD-19 evidence.
Carvallo, Andrés; Parra, Denis; Lobel, Hans; Rada, Gabriel.
Afiliação
  • Carvallo A; Centro Nacional de Inteligencia Artificial, Vicuña Mackenna 4686, Macul, Santiago, Región Metropolitana, Chile.
  • Parra D; Pontificia Universidad Católica de Chile, School of Engineering, Department of Computer Science, Vicuña Mackenna 4860, Macul 7820436, Región Metropolitana, Chile.
  • Lobel H; Pontificia Universidad Católica de Chile, School of Engineering, Department of Computer Science, Vicuña Mackenna 4860, Macul 7820436, Región Metropolitana, Chile.
  • Rada G; Pontificia Universidad Católica de Chile, School of Medicine, Avda. Libertador Bernando O'Higgins 340, Santiago 9170464, Región Metropolitana, Chile.
Data Brief ; 51: 109720, 2023 Dec.
Article em En | MEDLINE | ID: mdl-37965606
The COVID-19 pandemic has underlined the need for reliable information for clinical decision-making and public health policies. As such, evidence-based medicine (EBM) is essential in identifying and evaluating scientific documents pertinent to novel diseases, and the accurate classification of biomedical text is integral to this process. Given this context, we introduce a comprehensive, curated dataset composed of COVID-19-related documents. This dataset includes 20,047 labeled documents that were meticulously classified into five distinct categories: systematic reviews (SR), primary study randomized controlled trials (PS-RCT), primary study non-randomized controlled trials (PS-NRCT), broad synthesis (BS), and excluded (EXC). The documents, labeled by collaborators from the Epistemonikos Foundation, incorporate information such as document type, title, abstract, and metadata, including PubMed id, authors, journal, and publication date. Uniquely, this dataset has been curated by the Epistemonikos Foundation and is not readily accessible through conventional web-scraping methods, thereby attesting to its distinctive value in this field of research. In addition to this, the dataset also includes a vast evidence repository comprising 427,870 non-COVID-19 documents, also categorized into SR, PS-RCT, PS-NRCT, BS, and EXC. This additional collection can serve as a valuable benchmark for subsequent research. The comprehensive nature of this open-access dataset and its accompanying resources is poised to significantly advance evidence-based medicine and facilitate further research in the domain.
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Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Data Brief Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Chile País de publicação: Holanda

Texto completo: 1 Coleções: 01-internacional Base de dados: MEDLINE Idioma: En Revista: Data Brief Ano de publicação: 2023 Tipo de documento: Article País de afiliação: Chile País de publicação: Holanda