Machine Learning for COVID-19 needs global collaboration and data-sharing

The COVID-19 pandemic poses a historical challenge to society. The profusion of data requires machine learning to improve and accelerate COVID-19 diagnosis, prognosis and treatment. However, a global and open approach is necessary to avoid pitfalls in these applications.

Access options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

References

  1. 1.

    Dong, E., Du, H. & Gardner, L. Lancet Infect. Dis. 20, 533–534 (2020).

  2. 2.

    Dimensions COVID-19 publications, data sets, clinical trials. Figshare https://dimensions.figshare.com/articles/Dimensions_COVID-19_publications_datasets_and_clinical_trials/11961063 (2020).

  3. 3.

    Wu, Z. & McGoogan, J. M. JAMA 323, 1239–1242 (2020).

  4. 4.

    Claassen, J. et al. N. Engl. J. Med. 380, 2497–2505 (2019).

  5. 5.

    Sitt, J. D. et al. Brain 137, 2258–2270 (2014).

  6. 6.

    Peiffer-Smadja, N. et al. Clin. Microbiol. Infect. https://doi.org/10.1016/j.cmi.2019.09.009 (2019).

  7. 7.

    Ai, T. et al. Radiology https://doi.org/10.1148/radiol.2020200642 (2020).

  8. 8.

    Chen, Z. et al. Eur. J. Radiol. 126, 108972 (2020).

  9. 9.

    Pham, H. H., Le, T. T., Tran, D. Q., Ngo, D. T. & Nguyen, H. Q. Preprint at https://arxiv.org/abs/1911.06475 (2019).

  10. 10.

    Zheng, C. et al. Preprint at https://doi.org/10.1101/2020.03.12.20027185 (2020).

  11. 11.

    Chen, T., Kornblith, S., Norouzi, M. & Hinton, G. Preprint at https://arxiv.org/abs/2002.05709 (2020).

  12. 12.

    Belhadi, D. et al. Preprint at https://doi.org/10.1101/2020.03.18.20038190 (2020).

  13. 13.

    Liu, X. & Wang, X.-J. J. Genet. Genom. 47, 119–121 (2020).

  14. 14.

    Computational predictions of protein structures associated with COVID-19. Deepmind https://deepmind.com/research/open-source/computational-predictions-of-protein-structures-associated-with-COVID-19 (2020).

  15. 15.

    Peiffer-Smadja, N. et al. Clin. Microbiol. Infect. https://doi.org/10.1016/j.cmi.2020.02.006 (2020).

  16. 16.

    Stokes, J. M. et al. Cell 180, 688–702e13 (2020).

  17. 17.

    Weiskopf, D. et al. Preprint at https://doi.org/10.1101/2020.04.11.20062349 (2020).

  18. 18.

    Senior, A. W. et al. Nature 577, 706–710 (2020).

  19. 19.

    Gautret, P. et al. Int. J. Antimicrob. Agents https://doi.org/10.1016/j.ijantimicag.2020.105949 (2020).

  20. 20.

    COVID-19 Clinical Research Coalition Lancet 395, 1322–1325 (2020).

  21. 21.

    Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak. Wellcome Trust https://wellcome.ac.uk/coronavirus-covid-19/open-data (2020).

  22. 22.

    Open-access data and computational resources to address COVID-19. National Institutes of Health https://datascience.nih.gov/covid-19-open-access-resources (2020).

Download references

Author information

Affiliations

Authors

Corresponding author

Correspondence to Nathan Peiffer-Smadja.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Peiffer-Smadja, N., Maatoug, R., Lescure, F. et al. Machine Learning for COVID-19 needs global collaboration and data-sharing. Nat Mach Intell (2020). https://doi.org/10.1038/s42256-020-0181-6

Download citation