Prediction of Resting Energy Expenditure in Children: May Artificial Neural Networks Improve Our Accuracy?
children
energy expenditure
metabolism
neural networks
nutrition
Journal
Journal of clinical medicine
ISSN: 2077-0383
Titre abrégé: J Clin Med
Pays: Switzerland
ID NLM: 101606588
Informations de publication
Date de publication:
05 04 2020
05 04 2020
Historique:
received:
26
02
2020
revised:
22
03
2020
accepted:
03
04
2020
entrez:
9
4
2020
pubmed:
9
4
2020
medline:
9
4
2020
Statut:
epublish
Résumé
The inaccuracy of resting energy expenditure (REE) prediction formulae to calculate energy metabolism in children may lead to either under- or overestimated real caloric needs with clinical consequences. The aim of this paper was to apply artificial neural networks algorithms (ANNs) to REE prediction. We enrolled 561 healthy children (2-17 years). Nutritional status was classified according to World Health Organization (WHO) criteria, and 113 were obese. REE was measured using indirect calorimetry and estimated with WHO, Harris-Benedict, Schofield, and Oxford formulae. The ANNs considered specific anthropometric data to model REE. The mean absolute error (mean ± SD) of the prediction was 95.8 ± 80.8 and was strongly correlated with REE values (
Identifiants
pubmed: 32260581
pii: jcm9041026
doi: 10.3390/jcm9041026
pmc: PMC7230279
pii:
doi:
Types de publication
Journal Article
Langues
eng
Déclaration de conflit d'intérêts
The authors declare no conflict of interest.
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