ArticleBMC infectious diseases2022
Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol.
Article in BMC infectious diseases, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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Who cites it
14 citing papers in PubMed.
- Advancing Human-Centered AI in Clinical Decision Support: Sociocognitive Human-in-the-Loop Study in HIV Care.Journal of medical Internet research · 2026Article
- Temporal network analysis of comorbidities among people with HIV in South Carolina.HIV medicine · 2026Article
- Prevalence and factors associated with HIV drug resistance among adult persons living with HIV/AIDS in nine countries of Sub-Saharan Africa using population-based HIV impact assessments: 2015-2019.BMC public health · 2025Article
- Twenty-Five Years of Evolution and Hurdles in Electronic Health Records and Interoperability in Medical Research: Comprehensive Review.Journal of medical Internet research · 2025Review
- Longitudinal assessments of viral rebound among people with HIV in South Carolina: a population-based cohort study.AIDS care · 2025Article
- The Future of Gene Expression Studies in HIV Research.Current HIV research · 2025Review
- Longitudinal Viral Load Clustering for People With HIV Using Functional Principal Component Analysis.AIDS research and treatment · 2025Article
- The Impacts of HIV-Related Service Interruptions During the COVID-19 Pandemic: Protocol of a Mixed Methodology Longitudinal Study.AIDS and behavior · 2024Article
- Travel Burden and Timely Linkage to Care Among People Newly Diagnosed with HIV Infection in South Carolina from 2005 to 2020.AIDS and behavior · 2024Article
- Functional Multivariable Logistic Regression With an Application to HIV Viral Suppression Prediction.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Article
- Evaluating surveillance definitions of HIV viral suppression 2015-2019: Which definition best detected barriers to care?Journal of public health research · 2023Article
- Artificial Intelligence and Machine Learning Based Prediction of Viral Load and CD4 Status of People Living with HIV (PLWH) on Anti-Retroviral Treatment in Gedeo Zone Public Hospitals.International journal of general medicine · 2023Article
- Article
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Authors and funding
7 authors.
Funding
Abstract
backgroundGiven the importance of viral suppression in ending the HIV epidemic in the US and elsewhere, an optimal predictive model of viral status can help clinicians identify those at risk of poor viral control and inform clinical improvements in HIV treatment and care. With an increasing availability of electronic health record (EHR) data and social environmental information, there is a unique opportunity to improve our understanding of the dynamic pattern of viral suppression. Using a statewide cohort of people living with HIV (PLWH) in South Carolina (SC), the overall goal of the proposed research is to examine the dynamic patterns of viral suppression, develop optimal predictive models of various viral suppression indicators, and translate the models to a beta version of service-ready tools for clinical decision support.
methodsThe PLWH cohort will be identified through the SC Enhanced HIV/AIDS Reporting System (eHARS). The SC Office of Revenue and Fiscal Affairs (RFA) will extract longitudinal EHR clinical data of all PLWH in SC from multiple health systems, obtain data from other state agencies, and link the patient-level data with county-level data from multiple publicly available data sources. Using the deidentified data, the proposed study will consist of three operational phases: Phase 1: "Pattern Analysis" to identify the longitudinal dynamics of viral suppression using multiple viral load indicators; Phase 2: "Model Development" to determine the critical predictors of multiple viral load indicators through artificial intelligence (AI)-based modeling accounting for multilevel factors; and Phase 3: "Translational Research" to develop a multifactorial clinical decision system based on a risk prediction model to assist with the identification of the risk of viral failure or viral rebound when patients present at clinical visits. DISCUSSION: With both extensive data integration and data analytics, the proposed research will: (1) improve the understanding of the complex inter-related effects of longitudinal trajectories of HIV viral suppressions and HIV treatment history while taking into consideration multilevel factors; and (2) develop empirical public health approaches to achieve ending the HIV epidemic through translating the risk prediction model to a multifactorial decision system that enables the feasibility of AI-assisted clinical decisions.
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