AIMC Topic: Mothers

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Early childhood caries risk prediction using machine learning approaches in Bangladesh.

BMC oral health
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
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
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...

Predicting mothers' exclusive breastfeeding for the first 6 months: Interface creation study using machine learning technique.

Journal of evaluation in clinical practice
BACKGROUND: Machine learning techniques (MLT) build models to detect complex patterns and solve new problems using big data.

Classifying early infant feeding status from clinical notes using natural language processing and machine learning.

Scientific reports
The objective of this study is to develop and evaluate natural language processing (NLP) and machine learning models to predict infant feeding status from clinical notes in the Epic electronic health records system. The primary outcome was the classi...

Wife-Mother Role Conflict at the Critical Child-Rearing Stage: A Machine-Learning Approach to Identify What and How Matters in Maternal Depression Symptoms in China.

Prevention science : the official journal of the Society for Prevention Research
Maternal depression (MD) was one of the most prevalent psychiatric problems worldwide. However, it easily remains untreated and misses the best time to prevent the emergence or worsening of major depressive symptoms due to under-observed stigma and t...

Predicting exclusive breastfeeding in maternity wards using machine learning techniques.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Adequate support in maternity wards is decisive for breastfeeding outcomes during the first year of life. Quality improvement interventions require the identification of the factors influencing hospital benchmark indicators....

Machine learning approach to measurement of criticism: The core dimension of expressed emotion.

Journal of family psychology : JFP : journal of the Division of Family Psychology of the American Psychological Association (Division 43)
Expressed emotion (EE), a measure of the family's emotional climate, is a fundamental measure in caregiving research. A core dimension of EE is the level of criticism expressed by the caregiver to the care recipient, with a high level of criticism a ...

Predicting women with depressive symptoms postpartum with machine learning methods.

Scientific reports
Postpartum depression (PPD) is a detrimental health condition that affects 12% of new mothers. Despite negative effects on mothers' and children's health, many women do not receive adequate care. Preventive interventions are cost-efficient among high...

Identifying Factors Associated with Neonatal Mortality in Sub-Saharan Africa using Machine Learning.

AMIA ... Annual Symposium proceedings. AMIA Symposium
This study aimed at identifying the factors associated with neonatal mortality. We analyzed the Demographic and Health Survey (DHS) datasets from 10 Sub-Saharan countries. For each survey, we trained machine learning models to identify women who had ...