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NeuroCORD: A Language Model to Facilitate COVID-19-Associated Neurological Disorder Studies

Author

Listed:
  • Leihong Wu

    (National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR 72079, USA
    These authors contributed equally to this work.)

  • Syed Ali

    (National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR 72079, USA
    These authors contributed equally to this work.
    Current address: Integrative Nanotechnology Research Center, University of Arkansas at Little Rock, 2801 S University Ave, Little Rock, AR 72205, USA.)

  • Heather Ali

    (Department of Internal Medicine, University of Arkansas for Medical Sciences, 4301 West Markham, Little Rock, AR 72205, USA)

  • Tyrone Brock

    (National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR 72079, USA
    Department of Mathematics and Computer Science, University of Arkansas at Pine Bluff, 1200 University Drive, Pine Bluff, AR 71601, USA)

  • Joshua Xu

    (National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR 72079, USA)

  • Weida Tong

    (National Center for Toxicological Research, Food and Drug Administration, 3900 NCTR Rd., Jefferson, AR 72079, USA)

Abstract

COVID-19 can lead to multiple severe outcomes including neurological and psychological impacts. However, it is challenging to manually scan hundreds of thousands of COVID-19 articles on a regular basis. To update our knowledge, provide sound science to the public, and communicate effectively, it is critical to have an efficient means of following the most current published data. In this study, we developed a language model to search abstracts using the most advanced artificial intelligence (AI) to accurately retrieve articles on COVID-19-associated neurological disorders. We applied this NeuroCORD model to the largest benchmark dataset of COVID-19, CORD-19. We found that the model developed on the training set yielded 94% prediction accuracy on the test set. This result was subsequently verified by two experts in the field. In addition, when applied to 96,000 non-labeled articles that were published after 2020, the NeuroCORD model accurately identified approximately 3% of them to be relevant for the study of COVID-19-associated neurological disorders, while only 0.5% were retrieved using conventional keyword searching. In conclusion, NeuroCORD provides an opportunity to profile neurological disorders resulting from COVID-19 in a rapid and efficient fashion, and its general framework could be used to study other COVID-19-related emerging health issues.

Suggested Citation

  • Leihong Wu & Syed Ali & Heather Ali & Tyrone Brock & Joshua Xu & Weida Tong, 2022. "NeuroCORD: A Language Model to Facilitate COVID-19-Associated Neurological Disorder Studies," IJERPH, MDPI, vol. 19(16), pages 1-10, August.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:16:p:9974-:d:887055
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    References listed on IDEAS

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    1. Andres Carvallo & Denis Parra & Hans Lobel & Alvaro Soto, 2020. "Automatic document screening of medical literature using word and text embeddings in an active learning setting," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 3047-3084, December.
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