Blood-borne biomarkers may help predict COVID-19 mortality

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Abstract

A machine learning algorithm identifies blood-borne markers that are predictive of COVID-19 mortality.

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An interpretable mortality prediction model for COVID-19 patients

Li Yan, Hai-Tao Zhang, Jorge Gonçalves … (2020)

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Author and article information

Journal

Title: Science Translational Medicine

Abbreviated Title: Sci. Transl. Med.

Publisher: American Association for the Advancement of Science (AAAS)

ISSN (Print): 1946-6234

ISSN (Electronic): 1946-6242

Publication date Created: May 27 2020

Publication date (Electronic): May 27 2020

Publication date (Print): May 27 2020

Volume: 12

Issue: 545

Page: eabb7102

Affiliations

[1 ]Nancy E. and Peter C. Meinig School of Biomedical Engineering, Cornell University, Ithaca, NY 14850, USA. Email: vlaminck@cornell.edu

Article

DOI: 10.1126/scitranslmed.abb7102

SO-VID: 3664ed19-f43e-4196-9b04-540109a7e499

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