OASIS +: leveraging machine learning to improve the prognostic accuracy of OASIS severity score for predicting in-hospital mortality

BACKGROUND: Severity scores assess the acuity of critical illness by penalizing for the deviation of physiologic measurements from normal and aggregating these penalties (also called “weights” or “subscores”) into a final score (or probability) for quantifying the severity of critical illness (or th...

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Detalles Bibliográficos
Autores principales: EL-Manzalawy, Yasser, Abbas, Mostafa, Hoaglund, Ian, Cerna, Alvaro Ulloa, Morland, Thomas B., Haggerty, Christopher M., Hall, Eric S., Fornwalt, Brandon K.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118103/
https://www.ncbi.nlm.nih.gov/pubmed/33985483
http://dx.doi.org/10.1186/s12911-021-01517-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8118103/
https://www.ncbi.nlm.nih.gov/pubmed/33985483
http://dx.doi.org/10.1186/s12911-021-01517-7

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