AIMC Topic: Maternal Health
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Incorporating machine learning and statistical methods to address maternal healthcare disparities in US: A systematic review.
International journal of medical informatics
Apr 14, 2025
BACKGROUND: Maternal health disparities are recognized as a significant public health challenge, with pronounced disparities evident across racial, socioeconomic, and geographic dimensions. Although healthcare technologies have advanced, these dispar...
Mother: a maternal online technology for health care dataset.
BMC research notes
Apr 8, 2025
OBJECTIVES: These data enable the development of both textual and speech based conversational machine learning models that can be used by expectant mothers to provide answers to challenges they face during the different trimesters of their pregnancy....
Bridging gaps in artificial intelligence adoption for maternal-fetal and obstetric care: Unveiling transformative capabilities and challenges.
Computer methods and programs in biomedicine
Feb 23, 2025
PURPOSE: This review aims to comprehensively explore the application of Artificial Intelligence (AI) to an area that has not been traditionally explored in depth: the continuum of maternal-fetal health. In doing so, the intent was to examine this phy...
Automated approach for fetal and maternal health management using light gradient boosting model with SHAP explainable AI.
Frontiers in public health
Dec 20, 2024
Fetal health holds paramount importance in prenatal care and obstetrics, as it directly impacts the wellbeing of mother and fetus. Monitoring fetal health through pregnancy is crucial for identifying and addressing potential risks and complications t...
Predicting newborn birth outcomes with prenatal maternal health features and correlates in the United States: a machine learning approach using archival data.
BMC pregnancy and childbirth
Sep 17, 2024
BACKGROUND: Newborns are shaped by prenatal maternal experiences. These include a pregnant person's physical health, prior pregnancy experiences, emotion regulation, and socially determined health markers. We used a series of machine learning models ...
Ensemble machine learning framework for predicting maternal health risk during pregnancy.
Scientific reports
Sep 14, 2024
Maternal health risks can cause a range of complications for women during pregnancy. High blood pressure, abnormal glucose levels, depression, anxiety, and other maternal health conditions can all lead to pregnancy complications. Proper identificatio...
Modeling the relationship between maternal health and infant behavioral characteristics based on machine learning.
PloS one
Aug 20, 2024
This study investigates the impact of maternal health on infant development by developing a mathematical model that delineates the relationship between maternal health indicators and infant behavioral characteristics and sleep quality. The main contr...
Enabling pregnant women and their physicians to make informed medication decisions using artificial intelligence.
Journal of pharmacokinetics and pharmacodynamics
Apr 11, 2020
The role of artificial intelligence (AI) in healthcare for pregnant women. To assess the role of AI in women's health, discover gaps, and discuss the future of AI in maternal health. A systematic review of English articles using EMBASE, PubMed, and S...
Machine learning for the prediction of postpartum complications is promising, but needs rigorous evaluation.
BJOG : an international journal of obstetrics and gynaecology
Mar 18, 2019
From passive passenger to participating co-pilot - Pregnant women's expectations of being able to access their online journal from antenatal care.
Sexual & reproductive healthcare : official journal of the Swedish Association of Midwives
Nov 28, 2017
OBJECTIVE: The aim of the study was to describe pregnant women's expectations of being able to access their electronic health records from antenatal care.
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