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

应用深度学习预测泰国主要艾滋病毒高危人群中 PrEP 护理的保留情况以及 PrEP 的有效使用

• 批准号: 10619943
• 负责人: Rena Janamnuaysook
• 金额: $ 8.62万
• 依托单位: UNIV OF MASSACHUSETTS MED SCH WORCESTER
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10619943
• 关键词: AIDS prevention; Acquired Immunodeficiency Syndrome; Address; Adherence; Adopted; Affect; Age; Awareness; Behavioral; Caring; Categories; Certification; Client; Clinical; Communities; Complex; Country; Data; Data Sources; Development; Effectiveness; Epidemic; Feedback; Foundations; Goals; Guidelines; HIV; HIV Infections; HIV risk; High Risk Woman; Individual; Infection; Intervention; Knowledge; Logic; Machine Learning; Modeling; Morbidity - disease rate; National Institute of Mental Health; Outcome; Pattern; Performance; Persons; Play; Politics; Population; Populations at Risk; Predictive Factor; Prevention Research; Prevention strategy; Provider; Recommendation; Reporting; Research; Research Priority; Resource-limited setting; Risk; Risk Behaviors; Risk Factors; Role; Services; Solid; Statistical Data Interpretation; Subgroup; System; Techniques; Thailand; Training; Translating; United Nations; Visualization; clinical predictors; deep learning; deep learning model; design; effective intervention; follow-up; health disparity; high risk; improved; innovation; innovative technologies; machine learning prediction; member; men who have sex with men; mortality; peer; pre-exposure prophylaxis; predictive modeling; prevention effectiveness; programs; protective factors; scale up; service delivery; service providers; social; sociodemographics; therapy development; transgender women; uptake; usability

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.

项目概要/摘要 尽管艾滋病毒预防和治疗取得了巨大进展，但艾滋病毒仍然是发病和死亡的主要原因， 特别是针对重点人群 (KP)，包括男男性行为者 (MSM) 和跨性别女性 (TGW) 已证明暴露前预防 (PrEP) 可有效减少不同人群中的艾滋病毒感染。 然而，PrEP 作为联合预防策略的一部分实施时的有效性。 尽管已经做出了许多努力来评估依从性，但随着保留和依从性不佳而降低。 对于 PrEP 及其与 HIV 预防效果的关联，需要更多的研究来加深我们的认识 了解维持护理和坚持 PrEP 机器的个人层面的促进因素和障碍。 由于学习能够对复杂的非线性关系进行建模，因此有望有效解决这些问题 在许多相互作用的因素中不依赖于建模假设，以及深度学习的最新进展 尽管机器学习已经为各种临床预测应用带来了令人兴奋的结果。 学习已被应用于识别潜在的 PrEP 候选者，但在探索机器学习方面却做得很少， 特别是先进的深度学习技术，以评估 PrEP 护理保留的预测因素和 有效使用 PrEP。 为了缩小知识差距，拟议的研究旨在探索先进的机器学习技术 确定保留 PrEP 护理和在关键人群中有效使用 PrEP 的保护因素和风险因素 泰国，我们将进行描述性统计分析，以描述 MSM 和 MSM 之间的 PrEP 使用模式。 TGW（目标 1）；开发深度学习模型来预测 PrEP 护理和有效 PrEP 使用的后续损失 （目标 2）；并设计一个可解释的风险评分系统，用于识别具有高风险的客户 无效的 PrEP 使用，具有可解释的推理逻辑和相关的人口统计、行为、社会和 临床因素（目标 3）。 本研究响应 NIMH 在艾滋病毒预防方面的优先研究和战略目标 3.2，以制定 调整现有干预措施以优化结果的策略。 基于预测模型的评分系统将为定制干预措施提供信息，以优化 PrEP 参与和 促进差异化PrEP服务提供，为PrEP精准预防艾滋病奠定坚实基础 作为一种有效的策略。