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Birth Weight

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Predicting risks of low birth weight in Bangladesh with machine learning.

PloS one
BACKGROUND AND OBJECTIVE: Low birth weight is one of the primary causes of child mortality and several diseases of future life in developing countries, especially in Southern Asia. The main objective of this study is to determine the risk factors of ...

A Machine Learning-Based Intrauterine Growth Restriction (IUGR) Prediction Model for Newborns.

Indian journal of pediatrics
Intrauterine growth restriction (IUGR) is a condition in which the fetal weight is below the 10th percentile for its gestational age. Prenatal exposure to metals can cause a decrease in fetal growth during gestation thereby reducing birth weight. The...

Comparison of the data mining and machine learning algorithms for predicting the final body weight for Romane sheep breed.

PloS one
The current study aimed to predict final body weight (weight of fourth months of age to select the future reproducers) by using birth weight, birth type, sex, suckling weight, age at suckling weight, weaning weight, age at weaning weight, and age of ...

Issue of Data Imbalance on Low Birthweight Baby Outcomes Prediction and Associated Risk Factors Identification: Establishment of Benchmarking Key Machine Learning Models With Data Rebalancing Strategies.

Journal of medical Internet research
BACKGROUND: Low birthweight (LBW) is a leading cause of neonatal mortality in the United States and a major causative factor of adverse health effects in newborns. Identifying high-risk patients early in prenatal care is crucial to preventing adverse...

Infant death prediction using machine learning: A population-based retrospective study.

Computers in biology and medicine
BACKGROUND: Despite declines in infant death rates in recent decades in the United States, the national goal of reducing infant death has not been reached. This study aims to predict infant death using machine-learning approaches.

Deep learning-based segmentation of whole-body fetal MRI and fetal weight estimation: assessing performance, repeatability, and reproducibility.

European radiology
OBJECTIVES: To develop a deep-learning method for whole-body fetal segmentation based on MRI; to assess the method's repeatability, reproducibility, and accuracy; to create an MRI-based normal fetal weight growth chart; and to assess the sensitivity ...

Predicting long-term neurocognitive outcome after pediatric intensive care unit admission for bronchiolitis-preliminary exploration of the potential of machine learning.

European journal of pediatrics
PURPOSE: For successful prevention and intervention, it is important to unravel the complex constellation of factors that affect neurocognitive functioning after pediatric intensive care unit (PICU) admission. This study aims (1) to elucidate the pot...

An innovative supervised longitudinal learning procedure of recurrent neural networks with temporal data augmentation: Insights from predicting fetal macrosomia and large-for-gestational age.

Computers in biology and medicine
BACKGROUND: Longitudinal data in health informatics studies often present challenges due to sparse observations from each subject, limiting the application of contemporary deep learning for prediction. This issue is particularly relevant in predictin...

Development and evaluation of machine learning models for predicting large-for-gestational-age newborns in women exposed to radiation prior to pregnancy.

BMC medical informatics and decision making
INTRODUCTION: The correlation between radiation exposure before pregnancy and abnormal birth weight has been previously proven. However, for large-for-gestational-age (LGA) babies in women exposed to radiation before becoming pregnant, there is no pr...