Comparison of machine learning methods with logistic regression analysis in creating predictive models for risk of critical in-hospital events in COVID-19 patients on hospital admission

BACKGROUND: Machine learning (ML) algorithms have been trained to early predict critical in-hospital events from COVID-19 using patient data at admission, but little is known on how their performance compares with each other and/or with statistical logistic regression (LR). This prospective multicen...

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Detalles Bibliográficos
Autores principales: Sievering, Aaron W., Wohlmuth, Peter, Geßler, Nele, Gunawardene, Melanie A., Herrlinger, Klaus, Bein, Berthold, Arnold, Dirk, Bergmann, Martin, Nowak, Lorenz, Gloeckner, Christian, Koch, Ina, Bachmann, Martin, Herborn, Christoph U., Stang, Axel
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9702742/
https://www.ncbi.nlm.nih.gov/pubmed/36437469
http://dx.doi.org/10.1186/s12911-022-02057-4

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9702742/
https://www.ncbi.nlm.nih.gov/pubmed/36437469
http://dx.doi.org/10.1186/s12911-022-02057-4

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