Prediction of birth weight in pregnancy with gestational diabetes mellitus using an artificial neural network

摘要：

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 esti-mation 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.

机标关键词：prediction、network、weight、with、artificial、birth、diabetes、gestational、mellitus、neural

所属期刊栏目：23

分类号：F713.36;P445;TP18

在线出版日期：2022-05-20（万方平台首次上网日期，不代表论文的发表时间）

页数：共6页

页码：432-436,后插1-后插5