AIMC Topic: HIV Infections

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Ensemble learning of inverse probability weights for marginal structural modeling in large observational datasets.

Statistics in medicine
Inverse probability weights used to fit marginal structural models are typically estimated using logistic regression. However, a data-adaptive procedure may be able to better exploit information available in measured covariates. By combining predicti...

AI-enhanced telemedicine for personalized antiretroviral therapy in HIV patients with neurological comorbidities: a narrative review.

Postgraduate medical journal
BACKGROUND: Human immunodeficiency virus (HIV), while now manageable as a chronic health condition with highly active antiretroviral therapy (HAART), often precipitates the onset of neurological comorbidities such as HIV-associated neurocognitive dis...

Patterns in Mental Health Symptoms, Substance Use, and Viral Suppression in People with HIV: A Clustering Analysis.

AIDS and behavior
Mental health conditions and substance use are prevalent among people with HIV (PWH), are correlated with one another, and associate with viral non-suppression independently; their joint association with viral non-suppression may be under-studied bec...

Machine Learning-based Prediction of Active Tuberculosis in People With HIV Using Clinical Data.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
BACKGROUND: Coinfections of Mycobacterium tuberculosis (MTB) and human immunodeficiency virus (HIV) impose a substantial global health burden. Patients with MTB infection face a heightened risk of progression to incident active TB, which preventive t...

Machine learning algorithms to predict the risk of hyperlipidemia in people with HIV after starting HAART for 6 months.

AIDS (London, England)
OBJECTIVE: The purpose of this study was to use machine learning models to predict the risk of hyperlipidemia in people with HIV (PWH) for 6 months after starting HAART, to improve early intervention efforts and prevent further progression to cardiov...

Machine learning for personalized risk assessment of HIV, syphilis, gonorrhoea and chlamydia: A systematic review and meta-analysis.

International journal of infectious diseases : IJID : official publication of the International Society for Infectious Diseases
BACKGROUND: Machine learning (ML) shows promise for sexually transmitted infection (STI) risk prediction, but systematic evidence of its effectiveness remains fragmented.

Significant associations between high-risk sexual behaviors and enterotypes of gut microbiome in HIV-negative men who have sex with men.

mSphere
UNLABELLED: Gut microbiome of men who have sex with men (MSM) exhibits distinctive characteristics compared with general populations. The dysbiosis of the gut microbiome in MSM is also associated with the onset and evolution of HIV infection. Enterot...

Scalable and robust machine learning framework for HIV classification using clinical and laboratory data.

Scientific reports
Human Immunodeficiency Virus (HIV) is a retrovirus that weakens the immune system, increasing vulnerability to infections and cancers. HIV spreads primarily via sharing needles, from mother to child during childbirth or breastfeeding, or unprotected ...