Predicting acute clinical deterioration with interpretable machine learning to support emergency care decision making

The emergency department (ED) is a fast-paced environment responsible for large volumes of patients with varied disease acuity. Operational pressures on EDs are increasing, which creates the imperative to efficiently identify patients at imminent risk of acute deterioration. The aim of this study is...

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Detalles Bibliográficos
Autores principales: Boulitsakis Logothetis, Stelios, Green, Darren, Holland, Mark, Al Moubayed, Noura
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442440/
https://www.ncbi.nlm.nih.gov/pubmed/37604974
http://dx.doi.org/10.1038/s41598-023-40661-0

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10442440/
https://www.ncbi.nlm.nih.gov/pubmed/37604974
http://dx.doi.org/10.1038/s41598-023-40661-0

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