Machine learning research towards combating COVID-19: virus detection, spread prevention, and medical assistance

Osama Shahid, Mohammad Nasajpour, Seyedamin Pouriyeh*, Reza M. Parizi, Meng Han, Maria Valero, Fangyu Li, Mohammed Aledhari, Quan Z. Sheng

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

COVID-19 was first discovered in December 2019 and has continued to rapidly spread across countries worldwide infecting thousands and millions of people. The virus is deadly, and people who are suffering from prior illnesses or are older than the age of 60 are at a higher risk of mortality. Medicine and Healthcare industries have surged towards finding a cure, and different policies have been amended to mitigate the spread of the virus. While Machine Learning (ML) methods have been widely used in other domains, there is now a high demand for ML-aided diagnosis systems for screening, tracking, predicting the spread of COVID-19 and finding a cure against it. In this paper, we present a journey of what role ML has played so far in combating the virus, mainly looking at it from a screening, forecasting, and vaccine perspective. We present a comprehensive survey of the ML algorithms and models that can be used on this expedition and aid with battling the virus.

Original languageEnglish
Article number103751
Number of pages16
JournalJournal of Biomedical Informatics
Volume117
DOIs
Publication statusPublished - May 2021

Keywords

  • Artificial intelligence
  • COVID-19
  • Drug development
  • Healthcare
  • Machine learning
  • Predictive analysis

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