Prediction of Resting Energy Expenditure in Children: May Artificial Neural Networks Improve Our Accuracy?
Articolo
Data di Pubblicazione:
2020
Citazione:
Prediction of Resting Energy Expenditure in Children: May Artificial Neural Networks Improve Our Accuracy? / V.D. Cosmi, A. Mazzocchi, G.P. Milani, E. Calderini, S. Scaglioni, S. Bettocchi, V. D'Oria, T. Langer, G.C.I. Spolidoro, L. Leone, A. Battezzati, S. Bertoli, A. Leone, R.S.D. Amicis, A. Foppiani, C. Agostoni, E. Grossi. - In: JOURNAL OF CLINICAL MEDICINE. - ISSN 2077-0383. - 9:4(2020 Apr 05). [10.3390/jcm9041026]
Abstract:
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 (R2 = 0.88). The performance of ANNs was higher in the subgroup of obese children (101 ± 91.8) with a lower grade of imprecision (5.4%). ANNs as a novel approach may give valuable information regarding energy requirements and weight management in children.
Tipologia IRIS:
01 - Articolo su periodico
Keywords:
children; energy expenditure; metabolism; neural networks; nutrition
Elenco autori:
V.D. Cosmi, A. Mazzocchi, G.P. Milani, E. Calderini, S. Scaglioni, S. Bettocchi, V. D'Oria, T. Langer, G.C.I. Spolidoro, L. Leone, A. Battezzati, S. Bertoli, A. Leone, R.S.D. Amicis, A. Foppiani, C. Agostoni, E. Grossi
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