AIMC Topic: Uganda

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Assessing the performance of a point-of-need diagnostic algorithm in rapid detection of peripheral lymph node tuberculosis (Mobile-TB-Lab): a diagnostic evaluation study protocol.

BMJ open
INTRODUCTION: Early and accurate diagnosis of tuberculosis (TB) is central to ensuring the proper treatment and curbing the transmission of the disease. Despite the significant burden, the diagnosis of peripheral lymph node(LN)TB, the most prevalent ...

Mother: a maternal online technology for health care dataset.

BMC research notes
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....

Mental health phenotypes of well-controlled HIV in Uganda.

Frontiers in public health
INTRODUCTION: The phenotypic expression of mental health (MH) conditions among people with HIV (PWH) in Uganda and worldwide are heterogeneous. Accordingly, there has been a shift toward identifying MH phenotypes using data-driven methods capable of ...

Enhancing trauma triage in low-resource settings using machine learning: a performance comparison with the Kampala Trauma Score.

BMC emergency medicine
BACKGROUND: Traumatic injuries are a leading cause of morbidity and mortality globally, with a disproportionate impact on populations in low- and middle-income countries (LMICs). The Kampala Trauma Score (KTS) is frequently used for triage in these s...

Widespread use of ChatGPT and other Artificial Intelligence tools among medical students in Uganda: A cross-sectional study.

PloS one
BACKGROUND: Chat Generative Pre-trained Transformer (ChatGPT) is a 175-billion-parameter natural language processing model that uses deep learning algorithms trained on vast amounts of data to generate human-like texts such as essays. Consequently, i...

Machine learning-based prediction of antibiotic resistance in Mycobacterium tuberculosis clinical isolates from Uganda.

BMC infectious diseases
BACKGROUND: Efforts toward tuberculosis management and control are challenged by the emergence of Mycobacterium tuberculosis (MTB) resistance to existing anti-TB drugs. This study aimed to explore the potential of machine learning algorithms in predi...

Deep Learning to Predict Traumatic Brain Injury Outcomes in the Low-Resource Setting.

World neurosurgery
OBJECTIVE: Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these settings, accurate patient prognostication is both difficult and essential for high-quality patient care. With the ultimate goal of ...

Predicting the Individual Treatment Effect of Neurosurgery for Patients with Traumatic Brain Injury in the Low-Resource Setting: A Machine Learning Approach in Uganda.

Journal of neurotrauma
Traumatic brain injury (TBI) disproportionately affects low- and middle-income countries (LMICs). In these low-resource settings, effective triage of patients with TBI-including the decision of whether or not to perform neurosurgery-is critical in op...

Constrained binary classification using ensemble learning: an application to cost-efficient targeted PrEP strategies.

Statistics in medicine
Binary classification problems are ubiquitous in health and social sciences. In many cases, one wishes to balance two competing optimality considerations for a binary classifier. For instance, in resource-limited settings, an human immunodeficiency v...

Regulation of artificial intelligence in Uganda's healthcare: exploring an appropriate regulatory approach and framework to deliver universal health coverage.

International journal for equity in health
BACKGROUND: Uganda, like other United Nations (UN) member states, has undertaken to achieve Universal Health Coverage (UHC) by 2030 in line with Sustainable Development Goal (SDG) 3 targets. However, if this target is to be achieved, efforts will nee...