AIMC Topic: Pregnancy
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Effects of neonicotinoid pesticide exposure in the first trimester on gestational diabetes mellitus based on interpretable machine learning.
Environmental research
May 15, 2025
BACKGROUND: Gestational diabetes mellitus (GDM) is one of the most common pregnancy complications and seriously threatens the health of mothers and offspring. Neonicotinoids (NEOs) is a new class of pesticide and widely used worldwide. Prenatal NEOs ...
OB HUB: Remote Electronic Fetal Monitoring Surveillance.
MCN. The American journal of maternal child nursing
May 13, 2025
OBJECTIVE: The purpose of this project was to implement a remote fetal surveillance unit with increased vigilance and timelier responses to electronic fetal monitor tracings to improve neonatal outcomes and increase safety.
High throughput recurrent pregnancy loss screening: urine metabolic fingerprints LDI-MS and machine learning.
The Analyst
May 12, 2025
Infertility is a significant challenge faced by many families worldwide, with recurrent pregnancy loss (RPL) being a prevalent cause of infertility among women. This condition causes immense emotional and physical distress for affected individuals an...
Exploring machine learning algorithms to predict short birth intervals and identify its determinants among reproductive-age women in East Africa.
BMC pregnancy and childbirth
May 9, 2025
BACKGROUND: The occurrence of short birth intervals among reproductive-age women in East Africa is a critical public health issue, contributing to maternal and child health risks. Identifying the key factors that predict short birth intervals can hel...
Machine learning of blood haemoglobin and haematocrit levels via smartphone conjunctiva photography in Kenyan pregnant women: a clinical study protocol.
BMJ open
May 8, 2025
INTRODUCTION: Anaemia during pregnancy is a widespread health burden globally, especially in low- and middle-income countries, posing a serious risk to both maternal and neonatal health. The primary challenge is that anaemia is frequently undetected ...
Application of machine learning in identifying risk factors for low APGAR scores.
BMC pregnancy and childbirth
May 8, 2025
BACKGROUND: Identifying the risk factors for low APGAR scores at birth is critical for improving neonatal outcomes and guiding clinical interventions.
Predicting peripartum depression using elastic net regression and machine learning: the role of remnant cholesterol.
BMC pregnancy and childbirth
May 8, 2025
BACKGROUND: Traditional statistical methods have dominated research on peripartum depression (PPD), but innovative approaches may provide deeper insights. This study aims to predict the impact factors of PPD using elastic net regression (ENR) combine...
Comparative Analysis of Machine Learning Approaches for Fetal Movement Detection with Linear Acceleration and Angular Rate Signals.
Sensors (Basel, Switzerland)
May 7, 2025
Reduced fetal movement (RFM) can indicate that a fetus is at risk, but current monitoring methods provide only a "snapshot in time" of fetal health and require trained clinicians in clinical settings. To improve antenatal care, there is a need for co...
Machine Learning for Predicting Stillbirth: A Systematic Review.
Reproductive sciences (Thousand Oaks, Calif.)
May 1, 2025
Stillbirth is a major global issue, with over 5 million cases each year. The multifactorial nature of stillbirth makes it difficult to predict. Artificial intelligence (AI) and machine learning (ML) have the potential to enhance clinical decision-mak...
Prediction of hypertension and diabetes in twin pregnancy using machine learning model based on characteristics at first prenatal visit: national registry study.
Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology
May 1, 2025
OBJECTIVE: To develop a prediction model for hypertensive disorders of pregnancy (HDP) and gestational diabetes mellitus (GDM) in twin pregnancy using characteristics obtained at the first prenatal visit.
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