Correction to: Scientific Reports https://doi.org/10.1038/s41598-021-86735-9, published online 30 March 2021
The original version of this Article contained errors in the Affiliations.
Affiliation 1 was incorrectly given as “LTSI Laboratory, INSERM U1099, Université de Rennes 1, Rennes, France”. The correct affiliation is listed below:
Univ Rennes, CHU Rennes, INSERM, LTSI-UMR 1099, F-35000 Rennes, France
Affiliation 2 was incorrectly given as “Department of Emergency Medicine, Pontchaillou University Hospital, 35033, Rennes, France”. The correct affiliation is listed below:
Department of Emergency Medicine, CHU Rennes, F-35000 Rennes, France.
The original Article and accompanying Supplementary Information file have been corrected
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Gangloff, C., Rafi, S., Bouzillé, G. et al. Author Correction: Machine learning is the key to diagnose COVID-19: a proof-of-concept study. Sci Rep 11, 17577 (2021). https://doi.org/10.1038/s41598-021-97049-1
Published:
DOI: https://doi.org/10.1038/s41598-021-97049-1
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
