AIMC Topic: HIV Infections
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An explainable web application based on machine learning for predicting fragility fracture in people living with HIV: data from Beijing Ditan Hospital, China.
Frontiers in cellular and infection microbiology
Mar 14, 2025
PURPOSE: This study aimed to develop and validate a novel web-based calculator using machine learning algorithms to predict fragility fracture risk in People living with HIV (PLWH), who face increased morbidity and mortality from such fractures.
Predicting the immunological nonresponse to antiretroviral therapy in people living with HIV: a machine learning-based multicenter large-scale study.
Frontiers in cellular and infection microbiology
Mar 11, 2025
BACKGROUND: Although highly active antiretroviral therapy (HAART) has greatly enhanced the prognosis for people living with HIV (PLWH), some individuals fail to achieve adequate immune reconstitution, known as immunological nonresponse (INR), which i...
Prediction of new HIV infection in men who have sex with men based on machine learning: secondary analysis of a prospective cohort study from Western China.
Annals of medicine
Mar 10, 2025
OBJECTIVE: This study aimed to construct a model based on machine learning to predict new HIV infections in HIV-negative men who have sex with men (MSM).
Using machine learning to predict poor adherence to antiretroviral therapy among adolescents with HIV in low resource settings.
AIDS (London, England)
Feb 24, 2025
OBJECTIVES: Achieving optimal adherence to antiretroviral therapy (ART) and viral suppression is still insufficient for attaining the UNAIDS 95-95-95 target of 2030, especially among adolescents with HIV (AWHIV). This study sought to develop a model ...
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...
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