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

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

Abstract 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 R...

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Autores principales: Sam F. Greenbury, Kayleigh Ougham, Jinyi Wu, Cheryl Battersby, Chris Gale, Neena Modi, Elsa D. Angelini
Formato: article
Lenguaje:EN
Publicado: Nature Portfolio 2021
Materias:
Medicine
R
Science
Q
Acceso en línea:https://doaj.org/article/57c4b1daaf4645168fb16db8a186d777
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