Prediction of birth weight in pregnancy with gestational diabetes mellitus using an artificial neural network / 浙江大学学报(英文版)(B辑:生物医学和生物技术)
Journal of Zhejiang University. Science. B
;
(12): 432-436, 2022.
Artigo
em Inglês
| WPRIM
| ID: wpr-929072
ABSTRACT
Gestational diabetes mellitus (GDM) is common during pregnancy, with the prevalence reaching as high as 31.0% in some European regions (McIntyre et al., 2019). Dysfunction of the glucose metabolism in pregnancy can influence fetal growth via alteration of the intrauterine environment, resulting in an increased risk of abnormal offspring birth weight (McIntyre et al., 2019). Infants with abnormal birth weight will be faced with increased risks of neonatal complications in the perinatal period and chronic non-communicable diseases in childhood and adulthood (Mitanchez et al., 2015; McIntyre et al., 2019). Therefore, accurate estimation of birth weight for neonates from women with GDM is crucial for more sensible perinatal decision-making and improvement of perinatal outcomes. Timely antenatal intervention, with reference to accurately estimated fetal weight, may also decrease the risks of adverse long-term diseases.
Texto completo:
DisponíveL
Índice:
WPRIM (Pacífico Ocidental)
Assunto principal:
Peso ao Nascer
/
Redes Neurais de Computação
/
Diabetes Gestacional
/
Desenvolvimento Fetal
Tipo de estudo:
Estudo prognóstico
Limite:
Adulto
/
Feminino
/
Humanos
/
Lactente
/
Recém-Nascido
/
Gravidez
Idioma:
Inglês
Revista:
Journal of Zhejiang University. Science. B
Ano de publicação:
2022
Tipo de documento:
Artigo
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