AIMC Topic: HIV Infections
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Use of machine learning approaches to predict transition of retention in care among people living with HIV in South Carolina: a real-world data study.
AIDS care
Jun 4, 2024
Maintaining retention in care (RIC) for people living with HIV (PLWH) helps achieve viral suppression and reduce onward transmission. This study aims to identify the best machine learning model that predicts the RIC transition over time. Extracting f...
Improving HIV preexposure prophylaxis uptake with artificial intelligence and automation: a systematic review.
AIDS (London, England)
May 23, 2024
OBJECTIVES: To identify studies promoting the use of artificial intelligence (AI) or automation with HIV preexposure prophylaxis (PrEP) care and explore ways for AI to be used in PrEP interventions.
Machine learning for predicting cognitive deficits using auditory and demographic factors.
PloS one
May 14, 2024
IMPORTANCE: Predicting neurocognitive deficits using complex auditory assessments could change how cognitive dysfunction is identified, and monitored over time. Detecting cognitive impairment in people living with HIV (PLWH) is important for early in...
Predicting humoral responses to primary and booster SARS-CoV-2 mRNA vaccination in people living with HIV: a machine learning approach.
Journal of translational medicine
May 7, 2024
BACKGROUND: SARS-CoV-2 mRNA vaccines are highly immunogenic in people living with HIV (PLWH) on effective antiretroviral therapy (ART). However, whether viro-immunologic parameters or other factors affect immune responses to vaccination is debated. T...
The predictive accuracy of machine learning for the risk of death in HIV patients: a systematic review and meta-analysis.
BMC infectious diseases
May 6, 2024
BACKGROUND: Early prediction of mortality in individuals with HIV (PWH) has perpetually posed a formidable challenge. With the widespread integration of machine learning into clinical practice, some researchers endeavor to formulate models predicting...
DeepARV: ensemble deep learning to predict drug-drug interaction of clinical relevance with antiretroviral therapy.
NPJ systems biology and applications
May 6, 2024
Drug-drug interaction (DDI) may result in clinical toxicity or treatment failure of antiretroviral therapy (ARV) or comedications. Despite the high number of possible drug combinations, only a limited number of clinical DDI studies are conducted. Com...
Data mining and machine learning in HIV infection risk research: An overview and recommendations.
Artificial intelligence in medicine
Apr 30, 2024
In the contemporary era, the applications of data mining and machine learning have permeated extensively into medical research, significantly contributing to areas such as HIV studies. By reviewing 38 articles published in the past 15 years, the stud...
Predictive model and risk analysis for coronary heart disease in people living with HIV using machine learning.
BMC medical informatics and decision making
Apr 25, 2024
OBJECTIVE: This study aimed to construct a coronary heart disease (CHD) risk-prediction model in people living with human immunodeficiency virus (PLHIV) with the help of machine learning (ML) per electronic medical records (EMRs).
A Machine Learning Model for Identifying Sexual Health Influencers to Promote the Secondary Distribution of HIV Self-Testing Among Gay, Bisexual, and Other Men Who Have Sex With Men in China: Quasi-Experimental Study.
JMIR public health and surveillance
Apr 24, 2024
BACKGROUND: Sexual health influencers (SHIs) are individuals actively sharing sexual health information with their peers, and they play an important role in promoting HIV care services, including the secondary distribution of HIV self-testing (SD-HIV...
DeepDynaForecast: Phylogenetic-informed graph deep learning for epidemic transmission dynamic prediction.
PLoS computational biology
Apr 10, 2024
In the midst of an outbreak or sustained epidemic, reliable prediction of transmission risks and patterns of spread is critical to inform public health programs. Projections of transmission growth or decline among specific risk groups can aid in opti...
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