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Birth Weight

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Quantifying the Impacts of Pre- and Post-Conception TSH Levels on Birth Outcomes: An Examination of Different Machine Learning Models.

Frontiers in endocrinology
BACKGROUND: While previous studies identified risk factors for diverse pregnancy outcomes, traditional statistical methods had limited ability to quantify their impacts on birth outcomes precisely. We aimed to use a novel approach that applied differ...

Fetal birthweight prediction with measured data by a temporal machine learning method.

BMC medical informatics and decision making
BACKGROUND: Birthweight is an important indicator during the fetal development process to protect the maternal and infant safety. However, birthweight is difficult to be directly measured, and is usually roughly estimated by the empirical formulas ac...

Categorization of birth weight phenotypes for inclusion in genetic evaluations using a deep neural network.

Journal of animal science
Birth weight (BW) serves as a valuable indicator of the economically relevant trait of calving ease (CE), and erroneous data collection for BW could impact genetic evaluations for CE. The objective of the current study was to evaluate the use of deep...

Hybridized neural networks for non-invasive and continuous mortality risk assessment in neonates.

Computers in biology and medicine
Premature birth is the primary risk factor in neonatal deaths, with the majority of extremely premature babies cared for in neonatal intensive care units (NICUs). Mortality risk prediction in this setting can greatly improve patient outcomes and reso...

Identifying clinical phenotypes in extremely low birth weight infants-an unsupervised machine learning approach.

European journal of pediatrics
There is increasing evidence that patient heterogeneity significantly hinders advancement in clinical trials and individualized care. This study aimed to identify distinct phenotypes in extremely low birth weight infants. We performed an agglomerativ...

Bayesian nonparametric quantile process regression and estimation of marginal quantile effects.

Biometrics
Flexible estimation of multiple conditional quantiles is of interest in numerous applications, such as studying the effect of pregnancy-related factors on low and high birth weight. We propose a Bayesian nonparametric method to simultaneously estimat...

Single-Examination Risk Prediction of Severe Retinopathy of Prematurity.

Pediatrics
BACKGROUND AND OBJECTIVES: Retinopathy of prematurity (ROP) is a leading cause of childhood blindness. Screening and treatment reduces this risk, but requires multiple examinations of infants, most of whom will not develop severe disease. Previous wo...