AIMC Topic: Mothers
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Postpartum depression in Northeastern China: a cross-sectional study 6 weeks after giving birth.
Frontiers in public health
May 21, 2025
INTRODUCTION: Postpartum depression (PPD) is a prevalent mental health issue that poses significant challenges to maternal wellbeing and infant development. We aimed to determine the prevalence of PPD and to investigate its associated determinants an...
Investigating Protective and Risk Factors and Predictive Insights for Aboriginal Perinatal Mental Health: Explainable Artificial Intelligence Approach.
Journal of medical Internet research
Apr 30, 2025
BACKGROUND: Perinatal depression and anxiety significantly impact maternal and infant health, potentially leading to severe outcomes like preterm birth and suicide. Aboriginal women, despite their resilience, face elevated risks due to the long-term ...
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....
Evaluating AI-based breastfeeding chatbots: quality, readability, and reliability analysis.
PloS one
Mar 17, 2025
BACKGROUND: In recent years, expectant and breastfeeding mothers commonly use various breastfeeding-related social media applications and websites to seek breastfeeding-related information. At the same time, AI-based chatbots-such as ChatGPT, Gemini,...
Leveraging artificial intelligence for inclusive maternity care: Enhancing access for mothers with disabilities in Africa.
Women's health (London, England)
Mar 16, 2025
Women with disabilities face significant barriers in accessing maternal healthcare, which increases their risk of adverse pregnancy outcomes, particularly in Africa, where resources are limited. Artificial intelligence (AI) presents a unique opportun...
Predicting mother and newborn skin-to-skin contact using a machine learning approach.
BMC pregnancy and childbirth
Feb 18, 2025
BACKGROUND: Despite the known benefits of skin-to-skin contact (SSC), limited data exists on its implementation, especially its influencing factors. The current study was designed to use machine learning (ML) to identify the predictors of SSC.
Predicting early cessation of exclusive breastfeeding using machine learning techniques.
PloS one
Jan 9, 2025
BACKGROUND: Identification of mother-infant pairs predisposed to early cessation of exclusive breastfeeding is important for delivering targeted support. Machine learning techniques enable development of transparent prediction models that enhance cli...
Early childhood caries risk prediction using machine learning approaches in Bangladesh.
BMC oral health
Jan 8, 2025
BACKGROUND: In the last years, artificial intelligence (AI) has contributed to improving healthcare including dentistry. The objective of this study was to develop a machine learning (ML) model for early childhood caries (ECC) prediction by identifyi...
Prediction of delayed breastfeeding initiation among mothers having children less than 2 months of age in East Africa: application of machine learning algorithms.
Frontiers in public health
Sep 2, 2024
BACKGROUND: Delayed breastfeeding initiation is a significant public health concern, and reducing the proportion of delayed breastfeeding initiation in East Africa is a key strategy for lowering the Child Mortality rate. However, there is limited evi...
Machine learning algorithms to predict healthcare-seeking behaviors of mothers for acute respiratory infections and their determinants among children under five in sub-Saharan Africa.
Frontiers in public health
Jun 19, 2024
BACKGROUND: Acute respiratory infections (ARIs) are the leading cause of death in children under the age of 5 globally. Maternal healthcare-seeking behavior may help minimize mortality associated with ARIs since they make decisions about the kind and...
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