Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes

At enrollment, serological antibody measures are more predictive than demographic variables of subsequent intubation or death among hospitalized COVID-19 patients.

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Publication Details

Journal
Communications Medicine
Published
2024-11-26
DOI
https://doi.org/10.1038/s43856-024-00658-w
Citations
3
Primary Topic
SARS-CoV-2 and COVID-19 Research
Type
article
Field-Weighted Citation Impact
1.17

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article

Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes

Scott L. Zeger, Zoe O Demko, Justin Hardick, Michael J. Betenbaugh et al.
3 citations
Communications Medicine
SARS-CoV-2 and COVID-19 Research
1.17
article

Application of machine learning algorithms to identify serological predictors of COVID-19 severity and outcomes

Scott L. Zeger, Zoe O Demko, Justin Hardick, Michael J. Betenbaugh, Maria Isabel Trejo‐Zambrano, Han-Sol Park, Marta Escarra‐Senmarti, Trevor S. Johnston, Felipe Andrade, Christopher D. Heaney, Anna Yin, Santosh Dhakal, Maclaine A. Parish, Nora Pisanic, Sabra L. Klein, Yukari Carol Manabe, Stephen J. Gould, Kate Kruczynski, Andrew Pekosz, Andrew H. Karaba, Abhinaya Ganesan, Sarika K. Mullapudi, Christopher Caputo, Annukka A.R. Antar, Janna R. Shapiro, Andrea L. Cox, John S. Lee, Patrick J. Shea
article en
3 citations

Abstract

At enrollment, serological antibody measures are more predictive than demographic variables of subsequent intubation or death among hospitalized COVID-19 patients.

Communications MedicineVol. 4(1)
Johns Hopkins University (US), Kansas State University (US), Johns Hopkins Medicine (US)
Fisher Center for Alzheimer's Research Foundation, Johns Hopkins University, National Institutes of Health, National Cancer Institute
Good health and well-being
Openalex Percentile: Top 20%
SARS-CoV-2 and COVID-19 Research
1.17
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