IDEAS home Printed from https://ideas.repec.org/a/igg/jrqeh0/v11y2022i1p1-17.html
   My bibliography  Save this article

The Role of Machine Learning and Artificial Intelligence in Clinical Decisions and the Herbal Formulations Against COVID-19

Author

Listed:
  • Anita Venaik

    (Amity University, Noida, India)

  • Rinki Kumari

    (Hind Institute of Medical Sciences, India)

  • Utkarsh Venaik

    (Netaji Subhas University of Technology, India)

  • Anand Nayyar

    (Duy Tan University, Vietnam)

Abstract

COVID-19 causes global health problems, and new technologies have to be established to detect, anticipate, diagnose, screen, and even trace COVID-19 by all health care experts. Several database searches are carried out in this literature-based study on machine learning (ML), artificial intelligence, computer-based molecular docking analysis (CBMDA), COVID-19, and herbal docking analysis. In the battle against different infectious diseases, ML, AI and CBMDA's past supporting data are involved. These devices have now been updated with advanced features and are part of the SARS-CoV-2 screening, prediction, diagnosis, contact tracing, and drug/vaccine production healthcare industries. This article aims to comprehensively analyse the essential role of ML and AI, and CBMDA in the screening, prediction, contact tracing, and production of herbal drugs for this virus and its associated epidemic.

Suggested Citation

  • Anita Venaik & Rinki Kumari & Utkarsh Venaik & Anand Nayyar, 2022. "The Role of Machine Learning and Artificial Intelligence in Clinical Decisions and the Herbal Formulations Against COVID-19," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 11(1), pages 1-17, January.
    • Handle: RePEc:igg:jrqeh0:v:11:y:2022:i:1:p:1-17
    as

    Download full text from publisher

    File URL: http://services.igi-global.com/resolvedoi/resolve.aspx?doi=10.4018/IJRQEH.2022010107
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Zhi-yong Yang & Wing-pui Kong & Yue Huang & Anjeanette Roberts & Brian R. Murphy & Kanta Subbarao & Gary J. Nabel, 2004. "A DNA vaccine induces SARS coronavirus neutralization and protective immunity in mice," Nature, Nature, vol. 428(6982), pages 561-564, April.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Arvind Yadav & Vinod Kumar & Devendra Joshi & Dharmendra Singh Rajput & Haripriya Mishra & Basavaraj S. Paruti, 2023. "Hybrid Artificial Intelligence-Based Models for Prediction of Death Rate in India Due to COVID-19 Transmission," International Journal of Reliable and Quality E-Healthcare (IJRQEH), IGI Global, vol. 12(2), pages 1-15, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ramon Roozendaal & Laura Solforosi & Daniel J. Stieh & Jan Serroyen & Roel Straetemans & Anna Dari & Muriel Boulton & Frank Wegmann & Sietske K. Rosendahl Huber & Joan E. M. van der Lubbe & Jenny Hend, 2021. "SARS-CoV-2 binding and neutralizing antibody levels after Ad26.COV2.S vaccination predict durable protection in rhesus macaques," Nature Communications, Nature, vol. 12(1), pages 1-10, December.
    2. Hossein Hozhabri & Francesca Piceci Sparascio & Hamidreza Sohrabi & Leila Mousavifar & René Roy & Daniela Scribano & Alessandro De Luca & Cecilia Ambrosi & Meysam Sarshar, 2020. "The Global Emergency of Novel Coronavirus (SARS-CoV-2): An Update of the Current Status and Forecasting," IJERPH, MDPI, vol. 17(16), pages 1-35, August.
    3. Laura Solforosi & Lea M. M. Costes & Jeroen T. B. M. Tolboom & Katherine McMahan & Tochi Anioke & David Hope & Tetyana Murdza & Michaela Sciacca & Emily Bouffard & Julia Barrett & Cindy Wu & Nicole Ha, 2023. "Booster with Ad26.COV2.S or Omicron-adapted vaccine enhanced immunity and efficacy against SARS-CoV-2 Omicron in macaques," Nature Communications, Nature, vol. 14(1), pages 1-14, December.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:igg:jrqeh0:v:11:y:2022:i:1:p:1-17. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Journal Editor (email available below). General contact details of provider: https://www.igi-global.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.