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Using neural networks to support high-quality evidence mapping.

Thomas B Røst1, Laura Slaughter2, Øystein Nytrø2

  • 1Department of Computer Science, Norwegian University of Science and Technology (NTNU), Trondheim, Norway. brox@ntnu.no.

BMC Bioinformatics
|October 22, 2021
PubMed
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Deep learning aids the Norwegian Institute of Public Health (NIPH) in classifying COVID-19 research papers. This semi-automation improves screening scalability and maintains high-quality evidence maps for infection control.

Area of Science:

  • Infectious Disease Research
  • Health Informatics
  • Artificial Intelligence in Medicine

Background:

  • The Norwegian Institute of Public Health (NIPH) monitors COVID-19 publications for evidence-based infection prevention.
  • Current manual screening, coding, and summarization processes are time-intensive.
  • Existing workflows require incremental improvements for scalability.

Purpose of the Study:

  • To apply deep learning methods to automate classification and coding of COVID-19 literature.
  • To enhance the scalability of manual screening processes through semi-automation.
  • To maintain high-quality Evidence Map content amidst a rapid influx of publications.

Main Methods:

  • Utilized deep learning models to learn classification and coding from NIPH COVID-19 dashboard data.
Keywords:
Automated codingDeep learningEvidence based medicineEvidence mapsKnowledge disseminationMachine learning

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  • Employed text representations and neural network architectures for analysis.
  • Focused on classifying publication topic and type using titles and abstracts.

    • Main Results:

    • Demonstrated acceptable performance in classifying publication topic and type.
    • Indicated that simple neural network architectures can achieve desired results.
    • Provided early results on the effectiveness of deep learning in this context.

    Conclusions:

    • Deep learning methods show promise for semi-automating the screening of COVID-19 research.
    • The approach can aid experts in managing the growing volume of scientific literature.
    • Further development can enhance the efficiency and quality of evidence-based public health information.