Training machine learning models to predict 30-day mortality in patients discharged from the emergency department: a retrospective, population-based registry study

OBJECTIVES: The aim of this work was to train machine learning models to identify patients at end of life with clinically meaningful diagnostic accuracy, using 30-day mortality in patients discharged from the emergency department (ED) as a proxy. DESIGN: Retrospective, population-based registry stud...

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Detalles Bibliográficos
Autores principales: Blom, Mathias Carl, Ashfaq, Awais, Sant'Anna, Anita, Anderson, Philip D, Lingman, Markus
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2019
Materias:
Emergency Medicine
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701621/
https://www.ncbi.nlm.nih.gov/pubmed/31401594
http://dx.doi.org/10.1136/bmjopen-2018-028015

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6701621/
https://www.ncbi.nlm.nih.gov/pubmed/31401594
http://dx.doi.org/10.1136/bmjopen-2018-028015

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