AIMC Topic: Premature Birth

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Identification of cell senescence-related genes in spontaneous preterm birth based on bioinformatics analysis and machine learning.

PloS one
Spontaneous premature birth (SPTB) is a common pregnancy complication; however, few studies have explored cell senescence-related markers in SPTB. Bioinformatics and machine learning approaches were used to predict potential biomarkers associated wit...

Maternal lipidomic signatures of preterm and small-for-gestational-age newborn infants in low- and middle-income countries.

Science advances
Maternal lipid levels change dynamically during gestation to support normal fetal growth. To obtain a detailed footprint of these changes and their differences in pregnancies with preterm or small-for-gestational-age (SGA) neonates, we analyzed 641 l...

XGBoost-based analysis of maternal and biochemical factors associated with spontaneous preterm birth: a retrospective cohort study.

BMC pregnancy and childbirth
BACKGROUND: Spontaneous preterm birth (sPTB) remains a major cause of neonatal morbidity and early risk assessment was poor. This study aimed to evaluate the association and predictive potential of serum biomarkers and maternal factors with sPTB.

Prediction of preterm birth from cervical length measurements in twin pregnancies using machine learning.

Scientific reports
Multiple Cervical Length (CL) measurements are typically acquired throughout the course of twin pregnancy to detect the early stages of labour and identify pregnancies at a high risk of preterm delivery. This study uses Machine-Learning (ML) approach...

Association Between COVID-19 During Pregnancy and Preterm Birth by Trimester of Infection: Retrospective Cohort Study Using Large-Scale Social Media Data.

Journal of medical Internet research
BACKGROUND: Preterm birth, defined as birth at <37 weeks of gestation, is the leading cause of neonatal death globally and the second leading cause of infant mortality in the United States. There is mounting evidence that COVID-19 infection during pr...

Machine learning for the prediction of spontaneous preterm birth using early second and third trimester maternal blood gene expression: A cautionary tale.

PloS one
Spontaneous preterm birth (sPTB) remains a significant global health challenge and a leading cause of neonatal mortality and morbidity. Despite advancements in neonatal care, the prediction of sPTB remains elusive, in part due to complex etiologies a...

Extreme urban temperature exposure and preterm birth: Spatial-temporal risk zone prediction using machine learning models.

Environmental research
This study investigates temperature impacts on preterm birth (PTB) using residential address GPS coordinates for 311,972 pregnant women in Wuhan, China, coupled with daily environmental data. We developed a machine learning model to analyze the impac...

Novel Technologies in Preterm Birth Prediction: Current Advances and Ethical Challenges.

Journal of mother and child
Preterm birth (PTB) remains a significant challenge in modern obstetric practice, posing considerable risks to maternal and neonatal health. Despite advancements in medical technology, the incidence of PTB remains high, and its prediction continues t...

Preterm birth trends and risk factors in a multi-ethnic Asian population: A retrospective study from 2017 to 2023, can we screen and predict this?

Annals of the Academy of Medicine, Singapore
INTRODUCTION: Preterm birth (PTB) remains a leading cause of perinatal morbidity and mortality worldwide. Understanding Singapore's PTB trends and associated risk factors can inform effective strategies for screening and intervention. This study anal...

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...