Predicting mortality among septic patients presenting to the emergency department–a cross sectional analysis using machine learning

BACKGROUND: Sepsis is a life-threatening condition, causing almost one fifth of all deaths worldwide. The aim of the current study was to identify variables predictive of 7- and 30-day mortality among variables reflective of the presentation of septic patients arriving to the emergency department (E...

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Detalles Bibliográficos
Autores principales: Karlsson, Adam, Stassen, Willem, Loutfi, Amy, Wallgren, Ulrika, Larsson, Eric, Kurland, Lisa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2021
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276466/
https://www.ncbi.nlm.nih.gov/pubmed/34253184
http://dx.doi.org/10.1186/s12873-021-00475-7

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8276466/
https://www.ncbi.nlm.nih.gov/pubmed/34253184
http://dx.doi.org/10.1186/s12873-021-00475-7

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