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Studying patterns and predictors of HIV viral suppression using A Big Data approach: a research protocol
Jiajia Zhang1,2,3, Bankole Olatosi4,5,6, Xueying Yang2,3,7
1Department of Epidemiology and Biostatistics, Arnold School of Public Health, University of South Carolina, Columbia, SC, 29208, USA.
BMC Infectious Diseases
|February 5, 2022
Summary
This study develops AI-powered predictive models using electronic health records to identify people living with HIV at risk of poor viral suppression. These tools aim to improve HIV treatment and end the HIV epidemic.
Area of Science:
- Public Health
- Infectious Diseases
- Artificial Intelligence
Background:
- Viral suppression is crucial for ending the HIV epidemic.
- Electronic health record (EHR) data and social information offer opportunities to understand viral suppression dynamics.
- Predictive models can help clinicians identify individuals at risk of poor viral control.
Purpose of the Study:
- Examine dynamic patterns of viral suppression in people living with HIV (PLWH).
- Develop optimal predictive models for viral suppression indicators.
- Translate models into clinical decision support tools.
Main Methods:
- Identify PLWH cohort using the SC Enhanced HIV/AIDS Reporting System (eHARS).
- Extract and link longitudinal EHR data with county-level social determinants of health.
- Utilize AI-based modeling for pattern analysis and predictor identification.
- Develop a clinical decision system for risk prediction of viral failure or rebound.
Main Results:
- Analysis of longitudinal viral suppression dynamics.
- Identification of critical predictors for viral load indicators using AI.
- Development of a risk prediction model for clinical decision support.
Conclusions:
- Enhance understanding of HIV viral suppression trajectories and treatment history considering multilevel factors.
- Develop data-driven public health strategies to end the HIV epidemic.
- Enable AI-assisted clinical decisions through a translated risk prediction model.
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