Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children

Artificial Neural Network Algorithms to Predict Resting Energy Expenditure in Critically Ill Children

Introduction: Accurate assessment of resting energy expenditure (REE) can guide optimal nutritional prescription in critically ill children. Indirect calorimetry (IC) is the gold standard for REE measurement, but its use is limited. Alternatively, REE estimates by predictive equations/formulae are o...

Description complète

Enregistré dans:
Détails bibliographiques
Auteurs principaux: Giulia C. I. Spolidoro, Veronica D’Oria, Valentina De Cosmi, Gregorio Paolo Milani, Alessandra Mazzocchi, Alireza Akhondi-Asl, Nilesh M. Mehta, Carlo Agostoni, Edoardo Calderini, Enzo Grossi
Format: article
Langue:EN
Publié: MDPI AG 2021
Sujets:
energy expenditure
metabolism
nutrition
children
pediatrics
critical care
Nutrition. Foods and food supply
TX341-641
Accès en ligne:https://doaj.org/article/dea44dbd8ee1489487fc373d4b760e2e
Tags: Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!

Internet

https://doaj.org/article/dea44dbd8ee1489487fc373d4b760e2e

Documents similaires