AIMC Topic: HIV Infections
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Machine learning-driven in-hospital mortality prediction in HIV/AIDS patients with infection: a single-centred retrospective study.
Journal of medical microbiology
Nov 1, 2024
() 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
Sep 26, 2024
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)
Sep 1, 2024
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)
Jun 1, 2024
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
Sep 1, 2023
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...
Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning.
Bioinformatics (Oxford, England)
Sep 15, 2022
MOTIVATION: Knowing the sensitivity of a viral strain versus a monoclonal antibody is of interest for HIV vaccine development and therapy. The HIV strains vary in their resistance to antibodies, and the accurate prediction of virus-antibody sensitivi...
Chest X-ray Analysis With Deep Learning-Based Software as a Triage Test for Pulmonary Tuberculosis: An Individual Patient Data Meta-Analysis of Diagnostic Accuracy.
Clinical infectious diseases : an official publication of the Infectious Diseases Society of America
Apr 28, 2022
BACKGROUND: Automated radiologic analysis using computer-aided detection software (CAD) could facilitate chest X-ray (CXR) use in tuberculosis diagnosis. There is little to no evidence on the accuracy of commercially available deep learning-based CAD...
[Virological evolution of patients with HIV infection that start antiretroviral therapy with a very high baseline viral load].
Revista chilena de infectologia : organo oficial de la Sociedad Chilena de Infectologia
Dec 1, 2021
BACKGROUND: The degree of viral suppression in HIV patients who start antiretroviral therapy (ART) with very high viral loads (CV) is unknown.
Effectiveness and safety of elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil fumarate single-tablet combination among HIV-infected patients in Turkey: results from a real world setting.
African health sciences
Dec 1, 2021
BACKGROUND: Efficacy of elvitegravir/cobicistat/emtricitabine/tenofovir disoproxil (E/C/F/TDF) in treatment-naïve and experienced patients with HIV infection was demonstrated in phase 3 trials. The primary objective of this study was to evaluate effe...
Cohort-Derived Machine Learning Models for Individual Prediction of Chronic Kidney Disease in People Living With Human Immunodeficiency Virus: A Prospective Multicenter Cohort Study.
The Journal of infectious diseases
Oct 13, 2021
BACKGROUND: It is unclear whether data-driven machine learning models, which are trained on large epidemiological cohorts, may improve prediction of comorbidities in people living with human immunodeficiency virus (HIV).
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