User interface approaches implemented with automated patient deterioration surveillance tools: protocol for a scoping review

INTRODUCTION: Early identification of patients who may suffer from unexpected adverse events (eg, sepsis, sudden cardiac arrest) gives bedside staff valuable lead time to care for these patients appropriately. Consequently, many machine learning algorithms have been developed to predict adverse even...

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Detalles Bibliográficos
Autores principales: Wan, Yik-Ki Jacob, Del Fiol, Guilherme, McFarland, Mary M, Wright, Melanie C
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Health Informatics
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8762135/
https://www.ncbi.nlm.nih.gov/pubmed/35027423
http://dx.doi.org/10.1136/bmjopen-2021-055525

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8762135/
https://www.ncbi.nlm.nih.gov/pubmed/35027423
http://dx.doi.org/10.1136/bmjopen-2021-055525

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