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

Andrés Carvallo1, Denis Parra2, Hans Lobel2

  • 1Centro Nacional de Inteligencia Artificial, Vicuña Mackenna 4686, Macul, Santiago, Región Metropolitana, Chile.

Data in Brief
|November 15, 2023
PubMed

Insights

A new dataset of 20,047 COVID-19 documents aids evidence-based medicine (EBM) by classifying research types. This curated collection supports reliable clinical decisions and public health policy during pandemics.

Area of Science:

  • Biomedical Informatics
  • Public Health
  • Information Science

Background:

  • The COVID-19 pandemic highlighted the critical need for accurate information to guide clinical decisions and public health strategies.
  • Evidence-based medicine (EBM) relies on the effective identification and evaluation of scientific literature, especially for emerging diseases.
  • Accurate classification of biomedical text is fundamental to the EBM process.

Purpose of the Study:

  • To introduce a comprehensive, curated dataset of COVID-19-related documents to support evidence-based medicine.
  • To provide a valuable resource for classifying biomedical texts pertinent to novel diseases.
  • To offer a benchmark for future research in evidence synthesis and information retrieval.

Main Methods:

  • A dataset of 20,047 COVID-19 documents was meticulously labeled into five 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).
  • Document details including type, title, abstract, and metadata (PubMed ID, authors, journal, publication date) were collected.
  • An additional repository of 427,870 non-COVID-19 documents, also categorized, was compiled as a benchmark.

Main Results:

  • The curated dataset contains 20,047 labeled COVID-19 documents, categorized for EBM applications.
  • A large repository of 427,870 non-COVID-19 documents is available for comparative analysis and benchmarking.
  • The dataset is unique, curated by the Epistemonikos Foundation, and not accessible via standard web scraping.

Conclusions:

  • This open-access dataset significantly advances evidence-based medicine by providing a structured resource for COVID-19 research.
  • The availability of categorized COVID-19 and non-COVID-19 documents facilitates further research in evidence synthesis and biomedical text classification.
  • The dataset is poised to enhance the reliability of information for clinical decision-making and public health policy formulation.

Keywords:
Biomedical text classificationCovid-19Evidence based medicineNatural language processing

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