AIMC Topic: HIV Infections

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Development of a machine learning prediction model for loss to follow-up in HIV care using routine electronic medical records in a low-resource setting.

BMC medical informatics and decision making
BACKGROUND: Despite the global commitment to ending AIDS by 2030, the loss of follow-up (LTFU) in HIV care remains a significant challenge. To address this issue, a data-driven clinical decision tool is crucial for identifying patients at greater ris...

Evaluating machine learning algorithms for predicting HIV status among young Thai men who have sex with men.

BMJ health & care informatics
OBJECTIVE: This study aimed to develop machine learning (ML) models to predict HIV status and assessed the factors associated with HIV infection among young men who have sex with men (MSM) under the Universal Health Coverage (UHC) programme in Thaila...

"What Did You Say, ChatGPT?" The Use of AI in Black Women's HIV Self-Education: An Inductive Qualitative Data Analysis.

The Journal of the Association of Nurses in AIDS Care : JANAC
The emergence of widely accessible artificial intelligence (AI) chatbots such as ChatGPT presents unique opportunities and challenges in public health self-education. This study examined simulations with ChatGPT for its use in public education of sex...

Stigma Attitudes Toward HIV/AIDS From 2011 Through 2023 in Japan: Retrospective Study in Japan.

Journal of medical Internet research
BACKGROUND: Stigma associated with HIV/AIDS continues to be a major barrier to prevention, management, and care. HIV stigma can negatively influence health behaviors. Surveys of the general public in Japan also demonstrated substantial gaps in knowle...

Integrating different approaches for the identification of new disruptors of HIV-1 capsid multimerization.

Biochemical and biophysical research communications
Human Immunodeficiency Virus (HIV) belongs to the Lentivirus genus, Retroviridae family, enveloped by a lipid bilayer within which the capsid protein encases the viral genome, reverse transcriptase, and integrase proteins, key components for viral re...

Machine learning to improve HIV screening using routine data in Kenya.

Journal of the International AIDS Society
INTRODUCTION: Optimal use of HIV testing resources accelerates progress towards ending HIV as a global threat. In Kenya, current testing practices yield a 2.8% positivity rate for new diagnoses reported through the national HIV electronic medical rec...

A Machine Learning Model for Diagnosing Opportunistic Infections in HIV Patients: Broad Applicability Across Infection Types.

Journal of cellular and molecular medicine
Opportunistic infections (OIs) are the leading cause of hospitalisation and mortality among Human Immunodeficiency Virus-infected (HIV-infected) patients. The diverse pathogen types and intricate clinical manifestations associated present a formidabl...

Using Machine Learning Techniques to Predict Viral Suppression Among People With HIV.

Journal of acquired immune deficiency syndromes (1999)
BACKGROUND: This study aims to develop and examine the performance of machine learning (ML) algorithms in predicting viral suppression among statewide people living with HIV (PWH) in South Carolina.

Convolutional neural network using magnetic resonance brain imaging to predict outcome from tuberculosis meningitis.

PloS one
INTRODUCTION: Tuberculous meningitis (TBM) leads to high mortality, especially amongst individuals with HIV. Predicting the incidence of disease-related complications is challenging, for which purpose the value of brain magnetic resonance imaging (MR...