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Comprehensively identifying Long Covid articles with human-in-the-loop machine learning.

Robert Leaman1, Rezarta Islamaj1, Alexis Allot1

  • 1National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health 8600 Rockville Pike, Bethesda, MD 20894, USA.

Patterns (New York, N.Y.)
|December 6, 2022
PubMed
Summary
This summary is machine-generated.

Identifying scientific articles on Long Covid (post-acute-sequelae of SARS-CoV-2 infection) is difficult due to varied terminology. Our machine learning model improves detection of Long Covid research, revealing its widespread impact across body systems.

Keywords:
COVID-19Long Covidactive learningdata programmingmachine learningnatural language processingpost-acute sequelae of SARS-CoV-2 infectiontext classificationweak supervision

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Area of Science:

  • Computational biology
  • Infectious disease epidemiology
  • Medical informatics

Background:

  • A significant portion of COVID-19 survivors develop persistent multisystemic symptoms, termed Long Covid or post-acute-sequelae of SARS-CoV-2 infection.
  • The lack of standardized terminology complicates the identification and retrieval of relevant scientific literature on Long Covid.
  • Accurate identification of Long Covid research is crucial for understanding its scope and impact on survivors' daily lives.

Purpose of the Study:

  • To develop and evaluate a robust machine learning framework for accurately identifying scientific articles related to Long Covid.
  • To overcome the challenge of non-standardized terminology in Long Covid research.
  • To analyze the characteristics and prevalence of Long Covid research within a curated collection.

Main Methods:

  • An iterative human-in-the-loop machine learning framework was developed, integrating data programming with active learning.
  • A robust ensemble model was created to enhance specificity and sensitivity in identifying Long Covid literature.
  • The Long Covid Collection was analyzed to understand terminology usage and associated health conditions.

Main Results:

  • The developed machine learning model demonstrated superior specificity and sensitivity compared to existing methods for Long Covid article identification.
  • Analysis revealed that a majority of Long Covid articles do not explicitly use any name for the condition.
  • When named, 'Long Covid' is the most frequent term used in the literature, which is associated with diverse systemic disorders.

Conclusions:

  • The developed machine learning framework effectively addresses the challenge of identifying Long Covid research despite inconsistent terminology.
  • The findings highlight the pervasive nature of Long Covid, affecting multiple body systems and often lacking consistent naming in scientific publications.
  • The Long Covid Collection, updated weekly and searchable online, serves as a valuable resource for researchers in this field.