AIMC Topic: Africa South of the Sahara
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Machine learning approach and geospatial analysis to determine HIV infection, awareness status, and transmission knowledge among adults in Sub-Saharan Africa.
BMC research notes
Dec 23, 2024
BACKGROUND: HIV/AIDS remains a major public health challenge, in Sub-Saharan Africa (SSA). In 2020, 16% of people living with HIV did not know their HIV status in SSA. Understanding the geospatial distribution of HIV infection, awareness status, and ...
Designing AI for mental health diagnosis: challenges from sub-Saharan African value-laden judgements on mental health disorders.
Journal of medical ethics
Aug 21, 2024
Recently clinicians have become more reliant on technologies such as artificial intelligence (AI) and machine learning (ML) for effective and accurate diagnosis and prognosis of diseases, especially mental health disorders. These remarks, however, ap...
Predicting adverse birth outcome among childbearing women in Sub-Saharan Africa: employing innovative machine learning techniques.
BMC public health
Jul 29, 2024
BACKGROUND: Adverse birth outcomes, including preterm birth, low birth weight, and stillbirth, remain a major global health challenge, particularly in developing regions. Understanding the possible risk factors is crucial for designing effective inte...
Factors of acute respiratory infection among under-five children across sub-Saharan African countries using machine learning approaches.
Scientific reports
Jul 9, 2024
Symptoms of Acute Respiratory infections (ARIs) among under-five children are a global health challenge. We aimed to train and evaluate ten machine learning (ML) classification approaches in predicting symptoms of ARIs reported by mothers among child...
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...
Predicting Schistosomiasis Intensity in Africa: A Machine Learning Approach to Evaluate the Progress of WHO Roadmap 2030.
The American journal of tropical medicine and hygiene
May 21, 2024
The World Health Organization (WHO) 2030 Roadmap aims to eliminate schistosomiasis as a public health issue, targeting reductions in the heavy intensity of infections. Previous studies, however, have predominantly used prevalence as the primary indic...
Artificial intelligence and machine learning for early detection and diagnosis of colorectal cancer in sub-Saharan Africa.
Gut
Apr 13, 2022
Stable warfarin dose prediction in sub-Saharan African patients: A machine-learning approach and external validation of a clinical dose-initiation algorithm.
CPT: pharmacometrics & systems pharmacology
Dec 9, 2021
Warfarin remains the most widely prescribed oral anticoagulant in sub-Saharan Africa. However, because of its narrow therapeutic index, dosing can be challenging. We have therefore (a) evaluated and compared the performance of 21 machine-learning tec...
Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa.
BMC pregnancy and childbirth
Sep 7, 2021
BACKGROUND: Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, ad...
Task-sharing with artificial intelligence: a design hypothesis for an Emergency Unit in sub-Saharan Africa.
The Pan African medical journal
Apr 20, 2021
In sub-Saharan Africa, there is a significant unmet need for emergency care, with a shortage of trained providers. One model to increase the number of providers is to task-share: roles traditionally filled by clinicians are shared with lay workers wh...
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