# Applying Deep Learning for Predicting Retention in PrEP Care and Effective PrEP Use among Key Populations at Risk for HIV in Thailand

> **NIH NIH R03** · UNIV OF MASSACHUSETTS MED SCH WORCESTER · 2024 · $76,214

## Abstract

Project Summary/Abstract
HIV remains a major cause of morbidity and mortality despite great progress in HIV prevention and treatment,
especially for key populations (KPs), including men who have sex with men (MSM) and transgender women
(TGW). Pre-exposure prophylaxis (PrEP) has been shown effective in reducing HIV acquisition among different
populations when implemented as part of a combination prevention strategy. However, effectiveness of PrEP
decreases with suboptimal retention and adherence. While many efforts have been made to assess adherence
to PrEP and its associations with HIV prevention effectiveness, more research is needed to deepen our
understanding of individual-level facilitators and barriers to retention in care and adherence to PrEP. Machine
learning holds promise to address those effectively due to its ability to model complex non-linear relationships
among many interacting factors without relying on modeling assumptions, and recent advances in deep
learning have resulted in exciting results for a variety of clinical prediction applications. Although machine
learning has been applied to identify potential PrEP candidates, little is done in exploring machine learning,
especially advanced deep learning techniques, to assess predictive factors for retention in PrEP care and
effective PrEP use.
 To close gaps in knowledge, the proposed study aims to explore advanced machine learning techniques to
identify protective and risk factors for retention in PrEP care and effective PrEP use among key populations in
Thailand. We will perform descriptive statistical analysis to characterize PrEP use patterns among MSM and
TGW (Aim 1); develop deep learning models to predict loss to follow up in PrEP care and effective PrEP use
(Aim 2); and design an explainable risk scoring system for identifying clients at high risk of discontinuation and
non-effective PrEP use, with interpretable reasoning logic and associated demographic, behavioral, social, and
clinical factors (Aim 3).
 This study is responsive to NIMH’s priority research in HIV prevention and strategic goal 3.2 to develop
strategies for tailoring existing interventions to optimize outcomes. The findings from this study and the
prediction-model based scoring system will inform tailored interventions to optimize PrEP engagement and
facilitate differentiated PrEP service delivery, paving a solid foundation for precise HIV prevention using PrEP
as an effective strategy.

## Key facts

- **NIH application ID:** 10876893
- **Project number:** 5R03MH130275-02
- **Recipient organization:** UNIV OF MASSACHUSETTS MED SCH WORCESTER
- **Principal Investigator:** Rena Janamnuaysook
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $76,214
- **Award type:** 5
- **Project period:** 2023-07-01 → 2025-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10876893

## Citation

> US National Institutes of Health, RePORTER application 10876893, Applying Deep Learning for Predicting Retention in PrEP Care and Effective PrEP Use among Key Populations at Risk for HIV in Thailand (5R03MH130275-02). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10876893. Licensed CC0.

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