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Premature Birth

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Identification of key metabolism-related genes and pathways in spontaneous preterm birth: combining bioinformatic analysis and machine learning.

Frontiers in endocrinology
BACKGROUND: Spontaneous preterm birth (sPTB) is a global disease that is a leading cause of death in neonates and children younger than 5 years of age. However, the etiology of sPTB remains poorly understood. Recent evidence has shown a strong associ...

Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa: employing innovative machine learning techniques.

BMC public health
BACKGROUND: Adverse birth outcomes, including preterm birth, low birth weight, and stillbirth, remain a major global health challenge, particularly in developing regions. Understanding the possible risk factors is crucial for designing effective inte...

Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches.

Scientific reports
To predict preterm birth (PTB) in multiparous women, comparing machine learning approaches with traditional logistic regression. A population-based cohort study was conducted using data from the Ontario Better Outcomes Registry and Network (BORN). Th...

Machine learning model-based preterm birth prediction and clinical nomogram: A big retrospective cohort study.

International journal of gynaecology and obstetrics: the official organ of the International Federation of Gynaecology and Obstetrics
OBJECTIVE: This study sought to develop a multifactorial predictive model for preterm birth risk, with the goal of providing clinical practitioners with early prevention.

Predicting preterm birth using electronic medical records from multiple prenatal visits.

BMC pregnancy and childbirth
This study aimed to predict preterm birth in nulliparous women using machine learning and easily accessible variables from prenatal visits. Elastic net regularized logistic regression models were developed and evaluated using 5-fold cross-validation ...

Evaluation of Pregnancy Risks in Women with Subchorionic Hematoma Using Machine Learning Models.

Medical science monitor : international medical journal of experimental and clinical research
BACKGROUND Subchorionic hematoma (SCH) can lead to blood accumulation and potentially affect pregnancy outcomes. Despite being a relatively common finding in early pregnancy, the effects of SCH on pregnancy outcomes such as miscarriage, stillbirth, a...

Prediction of preterm birth using machine learning: a comprehensive analysis based on large-scale preschool children survey data in Shenzhen of China.

BMC pregnancy and childbirth
BACKGROUND: Preterm birth (PTB) is a significant cause of neonatal mortality and long-term health issues. Accurate prediction and timely prevention of PTB are essential for reducing associated child mortality and morbidity. Traditional predictive met...

Evaluating how different balancing data techniques impact on prediction of premature birth using machine learning models.

PloS one
Premature birth can be defined as birth before 37 weeks of gestation, which is a significant global health issue, being the main cause for neonatal deaths. In this work, we evaluate machine learning models for predicting premature birth using Brazili...

Predicting preterm birth using machine learning methods.

Scientific reports
Preterm birth is a significant public health concern, given its correlation with neonatal mortality and morbidity. The aetiology of preterm birth is complex and multifactorial. The objective of this study was to develop and compare machine learning m...

Machine learning prediction of preterm birth in women under 35 using routine biomarkers in a retrospective cohort study.

Scientific reports
Preterm birth (PTB), defined as delivery before 37 weeks, affects 15 million infants annually, accounting for 11% of live births and over 35% of neonatal deaths. While advanced maternal age (≥ 35 years) is a known risk factor, PTB risk in women under...