Utility of a Machine-Guided Tool for Assessing Risk Behaviour Associated With Contracting HIV in South Africa
Clinical research evaluating a machine learning-based HIV risk assessment tool using structured questionnaires and validation against HIV testing outcomes in South Africa.
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Background
Mobile technology has helped to advance health programs, and studies have shown that an automated risk prediction model can successfully be used to identify patients who exhibit a high probable risk of contracting human immunodeficiency virus (HIV). A machine-guided tool is an algorithm that takes a set of subjective and objective answers from a simple questionnaire and computes an HIV risk assessment score.
Objective
The primary objective of this study is to establish that machine learning can be used to develop machine-guided tools and give us a deeper statistical understanding of the correlation between certain behavioural patterns and HIV.
Methods
In total, 200 HIV-negative adult individuals across three South African study sites each (two semirural and one urban) will be recruited. Study processes will include (1) completing a series of questions (demographic, sexual behaviour and history, personal, lifestyle, and symptoms) on an application system, unaided; (2) two HIV tests (one per study visit) being performed by a nurse/counsellor according to South African national guidelines; and (3) communicating test results and completing a user experience survey questionnaire.
Results
Ethical approval was received from the University of Witwatersrand Human Research Ethics Committee (HREC; ethics reference no. 200312) on August 20, 2020. This study is ongoing. Data collection has commenced and is expected to be completed in the second half of 2021. We will report on the machine-guided tool's performance and usability, together with user satisfaction and recommendations for improvement.
Conclusions
Machine-guided risk assessment tools can provide a cost-effective alternative to large-scale HIV screening and help in providing targeted counseling and testing to prevent the spread of HIV.
Trial registration
South African National Clinical Trial Registry DOH-27-042021-679
IRRID
DERR1-10.2196/30304
Keywords
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Despite advances in reducing HIV-related mortality, persistently high HIV incidence rates are undermining global efforts to end the epidemic by 2030. Accurate and granular risk prediction is critical for these campaigns but is often lacking in regions where the burden is highest. Machine learning and artificial intelligence algorithms have proven effective at predicting the risk of HIV infection in both high resource and low resource settings.
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