Cargando…
Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19
A key task of emergency departments is to promptly identify patients who require hospital admission. Early identification ensures patient safety and aids organisational planning. Supervised machine learning algorithms can use data describing historical episodes to make ahead-of-time predictions of c...
Autores principales: | Duckworth, Christopher, Chmiel, Francis P., Burns, Dan K., Zlatev, Zlatko D., White, Neil M., Daniels, Thomas W. V., Kiuber, Michael, Boniface, Michael J. |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2021
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8626460/ https://www.ncbi.nlm.nih.gov/pubmed/34837021 http://dx.doi.org/10.1038/s41598-021-02481-y |
Ejemplares similares
-
Using explainable machine learning to identify patients at risk of reattendance at discharge from emergency departments
por: Chmiel, F. P., et al.
Publicado: (2021) -
Emergency departments reduce admissions
por: Osterland, Thomas, et al.
Publicado: (2012) -
Arrival by ambulance explains variation in mortality by time of admission: retrospective study of admissions to hospital following emergency department attendance in England
por: Anselmi, Laura, et al.
Publicado: (2017) -
Changes in admission thresholds in English emergency departments
por: Wyatt, Steven, et al.
Publicado: (2017) -
Impact of Geriatric Admissions on Workload in the Emergency Department
por: Kłosiewicz, Tomasz, et al.
Publicado: (2023)