Using machine learning to identify quality-of-care predictors for emergency caesarean sections: a retrospective cohort study

OBJECTIVES: Emergency caesarean sections (ECS) are time-sensitive procedures. Multiple factors may affect team efficiency but their relative importance remains unknown. This study aimed to identify the most important predictors contributing to quality of care during ECS in terms of the arrival-to-de...

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Detalles Bibliográficos
Autores principales: Andersen, Betina Ristorp, Ammitzbøll, Ida, Hinrich, Jesper, Lehmann, Sune, Ringsted, Charlotte Vibeke, Løkkegaard, Ellen Christine Leth, Tolsgaard, Martin G
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2022
Materias:
Obstetrics and Gynaecology
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8905885/
https://www.ncbi.nlm.nih.gov/pubmed/35256439
http://dx.doi.org/10.1136/bmjopen-2021-049046

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8905885/
https://www.ncbi.nlm.nih.gov/pubmed/35256439
http://dx.doi.org/10.1136/bmjopen-2021-049046

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