AIMC Topic: HIV Infections
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A comparative analysis of classical and machine learning methods for forecasting TB/HIV co-infection.
Scientific reports
Aug 16, 2024
TB/HIV coinfection poses a complex public health challenge. Accurate forecasting of future trends is essential for efficient resource allocation and intervention strategy development. This study compares classical statistical and machine learning mod...
Ameliorating Racial Disparities in HIV Prevention via a Nurse-Led, AI-Enhanced Program for Pre-Exposure Prophylaxis Utilization Among Black Cisgender Women: Protocol for a Mixed Methods Study.
JMIR research protocols
Aug 13, 2024
BACKGROUND: HIV pre-exposure prophylaxis (PrEP) is a critical biomedical strategy to prevent HIV transmission among cisgender women. Despite its proven effectiveness, Black cisgender women remain significantly underrepresented throughout the PrEP car...
"Where No One Has Gone Before": Questions to Ensure the Ethical, Rigorous, and Thoughtful Application of Artificial Intelligence in the Analysis of HIV Research.
The Journal of the Association of Nurses in AIDS Care : JANAC
Aug 6, 2024
ChatGPT, an artificial intelligence (AI) system released by OpenAI on November 30th, 2022, has upended scientific and educational paradigms, reshaping the way that we think about teaching, writing, and now research. Since that time, qualitative data ...
Optimal STI controls for HIV patients based on an efficient deep Q learning method.
Journal of theoretical biology
Aug 5, 2024
We investigate an efficient computational tool to suggest useful treatment regimens for people infected with the human immunodeficiency virus (HIV). Structured treatment interruption (STI) is a regimen in which therapeutic drugs are periodically admi...
Computer-aided prognosis of tuberculous meningitis combining imaging and non-imaging data.
Scientific reports
Jul 30, 2024
Tuberculous meningitis (TBM) is the most lethal form of tuberculosis. Clinical features, such as coma, can predict death, but they are insufficient for the accurate prognosis of other outcomes, especially when impacted by co-morbidities such as HIV i...
Computer-aided detection of tuberculosis from chest radiographs in a tuberculosis prevalence survey in South Africa: external validation and modelled impacts of commercially available artificial intelligence software.
The Lancet. Digital health
Jul 19, 2024
BACKGROUND: Computer-aided detection (CAD) can help identify people with active tuberculosis left undetected. However, few studies have compared the performance of commercially available CAD products for screening in high tuberculosis and high HIV se...
Early diagnosis of HIV cases by means of text mining and machine learning models on clinical notes.
Computers in biology and medicine
Jul 10, 2024
Undiagnosed and untreated human immunodeficiency virus (HIV) infection increases morbidity in the HIV-positive person and allows onward transmission of the virus. Minimizing missed opportunities for HIV diagnosis when a patient visits a healthcare fa...
Using machine learning models to plan HIV services: Emerging opportunities in design, implementation and evaluation.
South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde
Jun 24, 2024
HIV/AIDS remains one of the world's most significant public health and economic challenges, with approximately 36 million people currently living with the disease. Considerable progress has been made to reduce the impact of HIV/AIDS in the past years...
RAIN: machine learning-based identification for HIV-1 bNAbs.
Nature communications
Jun 24, 2024
Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoires is still lacking. Here, we develop a ...
Identification of hepatic steatosis among persons with and without HIV using natural language processing.
Hepatology communications
Jun 19, 2024
BACKGROUND: Steatotic liver disease (SLD) is a growing phenomenon, and our understanding of its determinants has been limited by our ability to identify it clinically. Natural language processing (NLP) can potentially identify hepatic steatosis syste...
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