AIMC Topic: HIV Infections
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Disease diagnostics using machine learning of B cell and T cell receptor sequences.
Science (New York, N.Y.)
Feb 21, 2025
Clinical diagnosis typically incorporates physical examination, patient history, various laboratory tests, and imaging studies but makes limited use of the human immune system's own record of antigen exposures encoded by receptors on B cells and T ce...
Predicting the Risk of HIV Infection and Sexually Transmitted Diseases Among Men Who Have Sex With Men: Cross-Sectional Study Using Multiple Machine Learning Approaches.
Journal of medical Internet research
Feb 20, 2025
BACKGROUND: Men who have sex with men (MSM) are at high risk for HIV infection and sexually transmitted diseases (STDs). However, there is a lack of accurate and convenient tools to assess this risk.
Mental health phenotypes of well-controlled HIV in Uganda.
Frontiers in public health
Jan 28, 2025
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 ...
The Application of Machine Learning Algorithms to Predict HIV Testing in Repeated Adult Population-Based Surveys in South Africa: Protocol for a Multiwave Cross-Sectional Analysis.
JMIR research protocols
Jan 27, 2025
BACKGROUND: HIV testing is the cornerstone of HIV prevention and a pivotal step in realizing the Joint United Nations Program on HIV/AIDS (UNAIDS) goal of ending AIDS by 2030. Despite the availability of relevant survey data, there exists a research ...
A graph neural network-based model with out-of-distribution robustness for enhancing antiretroviral therapy outcome prediction for HIV-1.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Jan 10, 2025
Predicting the outcome of antiretroviral therapies (ART) for HIV-1 is a pressing clinical challenge, especially when the ART includes drugs with limited effectiveness data. This scarcity of data can arise either due to the introduction of a new drug ...
STI/HIV risk prediction model development-A novel use of public data to forecast STIs/HIV risk for men who have sex with men.
Frontiers in public health
Jan 3, 2025
A novel automatic framework is proposed for global sexually transmissible infections (STIs) and HIV risk prediction. Four machine learning methods, namely, Gradient Boosting Machine (GBM), Random Forest (RF), XG Boost, and Ensemble learning GBM-RF-XG...
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 ...
Cytokine profiles as predictors of HIV incidence using machine learning survival models and statistical interpretable techniques.
Scientific reports
Dec 2, 2024
HIV remains a critical global health issue, with an estimated 39.9 million people living with the virus worldwide by the end of 2023 (according to WHO). Although the epidemic's impact varies significantly across regions, Africa remains the most affec...
Preferences for attributes of an artificial intelligence-based risk assessment tool for HIV and sexually transmitted infections: a discrete choice experiment.
BMC public health
Nov 21, 2024
INTRODUCTION: Early detection and treatment of HIV and sexually transmitted infections (STIs) are crucial for effective control. We previously developed MySTIRisk, an artificial intelligence-based risk tool that predicts the risk of HIV and STIs. We ...
Learning patterns of HIV-1 resistance to broadly neutralizing antibodies with reduced subtype bias using multi-task learning.
PLoS computational biology
Nov 20, 2024
The ability to predict HIV-1 resistance to broadly neutralizing antibodies (bnAbs) will increase bnAb therapeutic benefits. Machine learning is a powerful approach for such prediction. One challenge is that some HIV-1 subtypes in currently available ...
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