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A Survey of Using Machine Learning Algorithms During the COVID-19 Pandemic

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Emerging Technologies During the Era of COVID-19 Pandemic

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 348))

Abstract

The emergence of novel coronavirus (COVID-19) is considered a worldwide pandemic. In response to this pandemic and following the recent developments in artificial intelligence (AI) techniques, the literature witnessed an abundant amount of machine learning applications on COVID-19. To understand these applications, this study aims to provide an early review of the articles published on the employment of machine learning algorithms in predicting the COVID-19 infections, survival rates of patients, vaccine development, and drug discovery. While machine learning has had a more significant impact on healthcare, the analysis of the current review suggests that the use of machine learning is still in its early stages in fighting the COVID-19. Its practical application is hindered by the unavailability of large amounts of data. Other challenges, constraints, and future directions are also discussed.

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References

  1. Zhu, N., et al.: A novel coronavirus from patients with pneumonia in China, 2019. N. Engl. J. Med. (2020). https://doi.org/10.1056/NEJMoa2001017

    Article  Google Scholar 

  2. Belfiore, M.P., et al.: Artificial intelligence to codify lung CT in Covid-19 patients. Radiol. Medica (2020). https://doi.org/10.1007/s11547-020-01195-x

    Article  Google Scholar 

  3. Vaishya, R., Javaid, M., Khan, I.H., Haleem, A.: Artificial intelligence (AI) applications for COVID-19 pandemic. Diabetes Metab. Syndr. Clin. Res. Rev. (2020). https://doi.org/10.1016/j.dsx.2020.04.012

    Article  Google Scholar 

  4. Albahri, A.S., et al.: Role of biological data mining and machine learning techniques in detecting and diagnosing the novel coronavirus (COVID-19): a systematic review. J. Med. Syst. 44(7). (2020). https://doi.org/10.1007/s10916-020-01582-x

  5. Yu, K.H., Beam, A.L., Kohane, I.S.: Artificial intelligence in healthcare. Nat. Biomed. Eng. 2(10), 719–731 (2018). https://doi.org/10.1038/s41551-018-0305-z

    Article  Google Scholar 

  6. Long, J.B., Ehrenfeld, J.M.: The role of augmented intelligence (AI) in detecting and preventing the spread of novel coronavirus. J. Med. Syst. (2020). https://doi.org/10.1007/s10916-020-1536-6

    Article  Google Scholar 

  7. Neri, E., Miele, V., Coppola, F., Grassi, R.: Use of CT and artificial intelligence in suspected or COVID-19 positive patients: statement of the Italian Society of Medical and Interventional Radiology. Radiol. Medica (2020). https://doi.org/10.1007/s11547-020-01197-9

    Article  Google Scholar 

  8. Arpaci, I., et al.: Analysis of twitter data using evolutionary clustering during the COVID-19 pandemic. Comput. Mater. Contin. 65(1), 193–203 (2020). https://doi.org/10.32604/cmc.2020.011489

    Article  Google Scholar 

  9. Ahuja, A.S., Reddy, V.P., Marques, O.: Artificial intelligence and COVID-19: a multidisciplinary approach. Integr. Med. Res. (2020)

    Google Scholar 

  10. Rao, A.S.R.S., Vazquez, J.A.: Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey in the populations when cities/towns are under quarantine. Infect. Control Hosp. Epidemiol. (2020). https://doi.org/10.1017/ice.2020.61

  11. Maghdid, H.S., Ghafoor, K.Z., Sadiq, A.S., Curran, K., Rabie, K.: A novel AI-enabled framework to diagnose coronavirus COVID 19 using smartphone embedded sensors: design study (2020)

    Google Scholar 

  12. Metsky, H.C., Freije, C.A., Kosoko-Thoroddsen, T.-S.F., Sabeti, P.C., Myhrvold, C.: CRISPR-based COVID-19 surveillance using a genomically-comprehensive machine learning approach. bioRxiv (2020). https://doi.org/10.1101/2020.02.26.967026

  13. Qi, X., et al.: Machine learning-based CT radiomics model for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study. medRxiv (2020)

    Google Scholar 

  14. Yu, H., et al.: Data-driven discovery of clinical routes for severity detection of COVID-19 pediatric cases. medRxiv (2020)

    Google Scholar 

  15. Tang, Z., et al.: Severity assessment of coronavirus disease 2019 (COVID-19) using quantitative features from chest CT images (2020)

    Google Scholar 

  16. Zheng, C., et al.: Deep learning-based detection for COVID-19 from chest CT using weak label. medRxiv (2020)

    Google Scholar 

  17. Gozes, O., et al.: Rapid AI development cycle for the coronavirus (COVID-19) pandemic: initial results for automated detection & patient monitoring using deep learning CT image analysis (2020)

    Google Scholar 

  18. Li, L., et al.: Artificial intelligence distinguishes COVID-19 from community acquired pneumonia on chest CT. Radiology, 200905 (2020). https://doi.org/10.1148/radiol.2020200905

  19. Xu, X., et al.: Deep learning system to screen coronavirus disease 2019 pneumonia (2020)

    Google Scholar 

  20. Narin, A., Kaya, C., Pamuk, Z.: Automatic detection of coronavirus disease (COVID-19) using X-ray images and deep convolutional neural networks (2020)

    Google Scholar 

  21. Fong, S.J., Li, G., Dey, N., Crespo, R.G., Herrera-Viedma, E.: Composite monte carlo decision making under high uncertainty of novel coronavirus epidemic using hybridized deep learning and fuzzy rule induction. Appl. Soft Comput. 93, 1–14 (2020)

    Article  Google Scholar 

  22. Jia, L., Li, K., Jiang, Y., Guo, X., Zhao, T.: Prediction and analysis of coronavirus disease 2019 (2020)

    Google Scholar 

  23. Qiang, X.-L., Xu, P., Fang, G., Liu, W.-B., Kou, Z.: Using the spike protein feature to predict infection risk and monitor the evolutionary dynamic of coronavirus. Infect. Dis. Poverty 9(1), 1–8 (2020). https://doi.org/10.1186/s40249-020-00649-8

    Article  Google Scholar 

  24. Poole, L.: Seasonal influences on the spread of SARS-CoV-2 (COVID19), causality, and forecastabililty (3-15-2020). SSRN Electron. J. (2020). https://doi.org/10.2139/ssrn.3554746

    Article  Google Scholar 

  25. Bai, X., et al.: Predicting COVID-19 malignant progression with AI techniques. medRxiv (2020)

    Google Scholar 

  26. Yan, L., et al.: Prediction of criticality in patients with severe Covid-19 infection using three clinical features: a machine learning-based prognostic model with clinical data in Wuhan. medrxiv.org (2020)

    Google Scholar 

  27. Yan, L., et al.: Prediction of survival for severe Covid-19 patients with three clinical features: development of a machine learning-based prognostic model with clinical data in Wuhan. medRxiv (2020). https://doi.org/10.1101/2020.02.27.20028027

  28. Yan, L., et al.: A machine learning-based model for survival prediction in patients with severe COVID-19 infection (2020). https://doi.org/10.1101/2020.02.27.20028027

  29. Naudé, W.: Artificial intelligence versus COVID-19: limitations, constraints and pitfalls. Ai Soc. 1–5 (2020)

    Google Scholar 

  30. Ong, E., Wong, M.U., Huffman, A., He, Y.: COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning. bioRxiv (2020)

    Google Scholar 

  31. Prachar, M., et al.: COVID-19 vaccine candidates: prediction and validation of 174 SARS-CoV-2 epitopes. bioRxiv (2020). https://doi.org/10.1101/2020.03.20.000794

  32. Magar, R., Yadav, P., Farimani, A.B.: Potential neutralizing antibodies discovered for novel corona virus using machine learning (2020)

    Google Scholar 

  33. Patankar, S.: Deep learning-based computational drug discovery to inhibit the RNA dependent RNA polymerase: application to SARS-CoV and COVID-19 (2020). doi: https://doi.org/10.31219/osf.io/6kpbg

  34. Tang, B., He, F., Liu, D., Fang, M., Wu, Z., Xu, D.: AI-aided design of novel targeted covalent inhibitors against SARS-CoV-2. bioRxiv (2020)

    Google Scholar 

  35. Rajkomar, A., Dean, J., Kohane, I.: Machine learning in medicine. N. Engl. J. Med. (2019). https://doi.org/10.1056/NEJMra1814259

    Article  Google Scholar 

  36. Bullock, J., Pham, K.H., Lam, C.S.N., Luengo-Oroz, M.: Mapping the landscape of artificial intelligence applications against COVID-19 (2020). arXiv preprint: arXiv:2003.11336

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Author information

Authors and Affiliations

  1. Faculty of Engineering & IT, The British University in Dubai, Dubai, UAE

    Mostafa Al-Emran

  2. Department of Information Technology, Al Buraimi University College, Al Buraimi, Oman

    Mohammed N. Al-Kabi

  3. Polytechnic of Coimbra, ESTGOH, Rua General Santos Costa, 3400-124, Oliveira do Hospital, Portugal

    Gonçalo Marques

Authors
  1. Mostafa Al-Emran
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  2. Mohammed N. Al-Kabi
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  3. Gonçalo Marques
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Corresponding author

Correspondence to Mostafa Al-Emran .

Editor information

Editors and Affiliations

  1. Tokat Gaziosmanpasa University, Tokat, Turkey

    Ibrahim Arpaci

  2. The British University in Dubai, Dubai, United Arab Emirates

    Mostafa Al-Emran

  3. Universiti Malaysia Kelantan, Kelantan, Malaysia

    Mohammed A. Al-Sharafi

  4. Polytechnic of Coimbra, Rua General Santos Costa, Portugal

    Gonçalo Marques

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Al-Emran, M., Al-Kabi, M.N., Marques, G. (2021). A Survey of Using Machine Learning Algorithms During the COVID-19 Pandemic. In: Arpaci, I., Al-Emran, M., A. Al-Sharafi, M., Marques, G. (eds) Emerging Technologies During the Era of COVID-19 Pandemic. Studies in Systems, Decision and Control, vol 348. Springer, Cham. https://doi.org/10.1007/978-3-030-67716-9_1

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