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HIV Infections

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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.

Survival causal rule ensemble method considering the main effect for estimating heterogeneous treatment effects.

Statistics in medicine
With an increasing focus on precision medicine in medical research, numerous studies have been conducted in recent years to clarify the relationship between treatment effects and patient characteristics. The treatment effects for patients with differ...

Machine learning-driven in-hospital mortality prediction in HIV/AIDS patients with infection: a single-centred retrospective study.

Journal of medical microbiology
() is a widely disseminated betaherpesvirus that typically induces latant infections. In immunocompromised populations, especially transplant and HIV-infected patients, infection increases in-hospital mortality. Although machine learning models ha...

Development of a Machine Learning Modeling Tool for Predicting HIV Incidence Using Public Health Data From a County in the Southern United States.

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
BACKGROUND: Advancements in machine learning (ML) have improved the accuracy of models that predict human immunodeficiency virus (HIV) incidence. These models have used electronic medical records and registries. We aim to broaden the application of t...

Using Machine Learning to Identify Patients at Risk of Acquiring HIV in an Urban Health System.

Journal of acquired immune deficiency syndromes (1999)
BACKGROUND: Effective measures exist to prevent the spread of HIV. However, the identification of patients who are candidates for these measures can be a challenge. A machine learning model to predict risk for HIV may enhance patient selection for pr...

Results From a Pilot Study of an Automated Directly Observed Therapy Intervention Using Artificial Intelligence With Conditional Economic Incentives Among Young Adults With HIV.

Journal of acquired immune deficiency syndromes (1999)
BACKGROUND: Despite improvements in antiretroviral therapy (ART) availability, suboptimal adherence is common among youth with HIV (YWH) and can increase drug resistance and poor clinical outcomes. Our study examined an innovative mobile app-based in...

Efficacy and Tolerability of Tenofovir/Lamivudine/Dolutegravir among Antiretroviral Therapy Naive Human Immunodeficiency Virus Infected Patients of a Tertiary Care Center in Eastern India.

The Journal of the Association of Physicians of India
BACKGROUND: Although many drug regimens have been used in the treatment of human immunodeficiency virus (HIV) infection, the National AIDS Control Organization (NACO) of India recommends the use of a fixed-dose combination of tenofovir/lamivudine/dol...