Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning

We used agnostic, unsupervised machine learning to cluster a large clinical database of information on infants admitted to neonatal units in England. Our aim was to obtain insights into nutritional practice, an area of central importance in newborn care, utilising the UK National Neonatal Research D...

Descripción completa

Detalles Bibliográficos
Autores principales: Greenbury, Sam F., Ougham, Kayleigh, Wu, Jinyi, Battersby, Cheryl, Gale, Chris, Modi, Neena, Angelini, Elsa D.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8009880/
https://www.ncbi.nlm.nih.gov/pubmed/33785776
http://dx.doi.org/10.1038/s41598-021-85878-z

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8009880/
https://www.ncbi.nlm.nih.gov/pubmed/33785776
http://dx.doi.org/10.1038/s41598-021-85878-z

Ejemplares similares