AI Medical Compendium Topic

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Africa, Eastern

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Exploring machine learning algorithms to predict short birth intervals and identify its determinants among reproductive-age women in East Africa.

BMC pregnancy and childbirth
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...

Neural networks differentiate between Middle and Later Stone Age lithic assemblages in eastern Africa.

PloS one
The Middle to Later Stone Age transition marks a major change in how Late Pleistocene African populations produced and used stone tool kits, but is manifest in various ways, places and times across the continent. Alongside changing patterns of raw ma...

Use of mobile technology to identify behavioral mechanisms linked to mental health outcomes in Kenya: protocol for development and validation of a predictive model.

BMC research notes
OBJECTIVE: This study proposes to identify and validate weighted sensor stream signatures that predict near-term risk of a major depressive episode and future mood among healthcare workers in Kenya.

Predicting place of delivery choice among childbearing women in East Africa: a comparative analysis of advanced machine learning techniques.

Frontiers in public health
BACKGROUND: Sub-Saharan Africa faces high neonatal and maternal mortality rates due to limited access to skilled healthcare during delivery. This study aims to improve the classification of health facilities and home deliveries using advanced machine...

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...

Prediction of incomplete immunization among under-five children in East Africa from recent demographic and health surveys: a machine learning approach.

Scientific reports
The World Health Organization as part of the goal of universal vaccination coverage by 2030 for all individuals. The global under-five mortality rate declined from 59% in 1990 to 38% in 2019, due to high immunization coverage. Despite the significant...

Exploring Machine Learning Algorithms to Predict Diarrhea Disease and Identify its Determinants among Under-Five Years Children in East Africa.

Journal of epidemiology and global health
BACKGROUND: The second most common cause of death for children under five is diarrhea. Early Predicting diarrhea disease and identify its determinants (factors) using an advanced machine learning model is the most effective way to save the lives of c...

Explainable artificial intelligence models for predicting pregnancy termination among reproductive-aged women in six east African countries: machine learning approach.

BMC pregnancy and childbirth
Pregnancy termination remains a complex and sensitive issue with approximately 45% of abortions worldwide being unsafe, and 97% of abortions occurring in developing countries. Unsafe pregnancy terminations have implications for women's reproductive h...

Exploring machine learning algorithms to predict not using modern family planning methods among reproductive age women in East Africa.

BMC health services research
BACKGROUND: The use of the modern family planning method provides chances for women to reach optimal child spacing, increase quality of life, increase economic status, achieve the desired family size, and prevent unsafe abortions and maternal and per...

Employing machine learning models to predict pregnancy termination among adolescent and young women aged 15-24 years in East Africa.

Scientific reports
Pregnancy termination is still a sensitive and continuing public health issue due to several political, economic, religious, and social concerns. This study assesses the applications of machine learning models in the prediction of pregnancy terminati...