<p>A continuous benchmarking community challenge for COVID-19 outcome prediction</p>

Yao Yan; Thomas  Schaffter; Timothy Bergquist; Thomas Yu; Vikas Vejaper; Noah Hammarlund; Justin Prosser; Justin Guinney; Sean  Mooney

doi:10.7490/f1000research.1118423.1

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Yan Y, Schaffter T, Bergquist T et al. A continuous benchmarking community challenge for COVID-19 outcome prediction [version 1; not peer reviewed]. F1000Research 2020, 9:1425 (poster) (https://doi.org/10.7490/f1000research.1118423.1)

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A continuous benchmarking community challenge for COVID-19 outcome prediction

Yao Yan, Thomas Schaffter, Timothy Bergquist, Thomas Yu, Vikas Vejaper, Noah Hammarlund, Justin Prosser, Justin Guinney¹, Sean Mooney²

Published 08 Dec 2020 (https://doi.org/10.7490/f1000research.1118423.1)

Author Affiliations

¹ Sage Bionetworks, USA
² University of Washigton, USA

Copyright © 2020 Yan Y et al. This is an open access work distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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CITE

Published 08 Dec 2020

A continuous benchmarking community challenge for COVID-19 outcome prediction
[version 1; not peer reviewed]

Yao Yan, Thomas Schaffter, Timothy Bergquist, Thomas Yu, Vikas Vejaper, Noah Hammarlund, Justin Prosser, Justin Guinney¹, Sean Mooney²

Author Affiliations

¹ Sage Bionetworks, USA
² University of Washigton, USA

Presented at

Pacific Symposium on Biocomputing (PSB) 2021

Abstract

Competing Interests

No competing interests were disclosed

Keywords

machine learning, model benchmarking, EHR data, COVID-19

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