Ensemble of deep learning language models to support the creation of living systematic reviews for the COVID-19 literature.

Journal: Systematic reviews
Published Date: Jun 5, 2023

Abstract

BACKGROUND: The COVID-19 pandemic has led to an unprecedented amount of scientific publications, growing at a pace never seen before. Multiple living systematic reviews have been developed to assist professionals with up-to-date and trustworthy health information, but it is increasingly challenging for systematic reviewers to keep up with the evidence in electronic databases. We aimed to investigate deep learning-based machine learning algorithms to classify COVID-19-related publications to help scale up the epidemiological curation process.

Authors

  • Julien Knafou
    Geneva School of Business Administration, CH-1227, University of Applied Sciences and Arts Western Switzerland, HES-SO, Geneva, Switzerland.
  • Quentin Haas
    Risklick AG, Spin-off University of Bern, Bern, Switzerland.
  • Nikolay Borissov
    Risklick AG, Spin-off University of Bern, Bern, Switzerland.
  • Michel Counotte
    Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
  • Nicola Low
    Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
  • Hira Imeri
    Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
  • Aziz Mert Ipekci
    Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
  • Diana Buitrago-Garcia
    Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
  • Leonie Heron
    Institute of Social and Preventive Medicine, University of Bern, Bern, Switzerland.
  • Poorya Amini
    Risklick AG, Spin-off University of Bern, Bern, Switzerland.
  • Douglas Teodoro
    Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland.

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