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Uganda

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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 ...

Detection of tuberculosis patterns in digital photographs of chest X-ray images using Deep Learning: feasibility study.

The international journal of tuberculosis and lung disease : the official journal of the International Union against Tuberculosis and Lung Disease
OBJECTIVE: To evaluate the feasibility of Deep Learning-based detection and classification of pathological patterns in a set of digital photographs of chest X-ray (CXR) images of tuberculosis (TB) patients.

Super learner analysis of real-time electronically monitored adherence to antiretroviral therapy under constrained optimization and comparison to non-differentiated care approaches for persons living with HIV in rural Uganda.

Journal of the International AIDS Society
INTRODUCTION: Real-time electronic adherence monitoring (EAM) systems could inform on-going risk assessment for HIV viraemia and be used to personalize viral load testing schedules. We evaluated the potential of real-time EAM (transferred via cellula...

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...

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 ...

Using artificial intelligence on dermatology conditions in Uganda: a case for diversity in training data sets for machine learning.

African health sciences
BACKGROUND: In pursuit of applying universal non-biased Artificial Intelligence (AI) in healthcare, it is essential that data from different geographies are represented.

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...

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...

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....