ArticleScientific reports2021
Identification of variation in nutritional practice in neonatal units in England and association with clinical outcomes using agnostic machine learning.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Cost-consequence analysis of early full milk feeding versus gradual feeding with intravenous support in preterm infants: results from the FEED1 trial.Archives of disease in childhood. Fetal and neonatal edition · 2026Trial
- Article
- Growth disparities among NICUs: insights from the New Jersey statewide neonatal collaborative.Journal of perinatology : official journal of the California Perinatal Association · 2026Article
- AI to predict extrauterine growth restriction during transitional nutrition of preterm infants: a retrospective study.Journal of perinatology : official journal of the California Perinatal Association · 2026Observational
- Nutritional Strategies for Preterm Neonates and Preterm Neonates Undergoing Surgery: New Insights for Practice and Wrong Beliefs to Uproot.Nutrients · 2024Review
- The association of different types of human milk with bronchopulmonary dysplasia in preterm infants.Frontiers in nutrition · 2024Article
- The past, current, and future of neonatal intensive care units with artificial intelligence: a systematic review.NPJ digital medicine · 2023Review
- Extrauterine Growth Restriction and Optimal Growth of Very Preterm Neonates: State of the Art.Nutrients · 2023Article
- A descriptive analysis of human milk dispensed by the Leipzig Donor Human Milk Bank for neonates between 2012 and 2019.Frontiers in nutrition · 2023Article
- Extending artificial intelligence research in the clinical domain: a theoretical perspective.Annals of operations research · 2022Article
- Using cluster analysis to describe phenotypical heterogeneity in extremely preterm infants: a retrospective whole-population study.BMJ open · 2022Article
- Incorporating parent, former patient and clinician perspectives in the design of a national UK double-cluster, randomised controlled trial addressing uncertainties in preterm nutrition.BMJ paediatrics open · 2021Article
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Authors and funding
7 authors.
Funding
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 Research Database (NNRD). We performed clustering on time-series data of daily nutritional intakes for very preterm infants born at a gestational age less than 32 weeks (n = 45,679) over a six-year period. This revealed 46 nutritional clusters heterogeneous in size, showing common interpretable clinical practices alongside rarer approaches. Nutritional clusters with similar admission profiles revealed associations between nutritional practice, geographical location and outcomes. We show how nutritional subgroups may be regarded as distinct interventions and tested for associations with measurable outcomes. We illustrate the potential for identifying relationships between nutritional practice and outcomes with two examples, discharge weight and bronchopulmonary dysplasia (BPD). We identify the well-known effect of formula milk on greater discharge weight as well as support for the plausible, but insufficiently evidenced view that human milk is protective against BPD. Our framework highlights the potential of agnostic machine learning approaches to deliver clinical practice insights and generate hypotheses using routine data.
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