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; 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.

项目摘要/摘要 艾滋病毒仍然是发病率和死亡任务的主要原因 特别是对于关键人群（KP），包括与男性发生性关系（MSM）和变性女性的男人 （TGW）。暴露前预防（PREP）已显示有效地减少不同的HIV获取 作为组合预防策略的一部分实施的种群。但是，准备的有效性 随着次优的保留和依从性而减小。尽管已经做出了许多努力来评估依从性 为了准备及其与艾滋病毒预防有效性的关联，需要更多的研究来加深我们 了解个人级别的促进者以及保留在护理和遵守准备方面的障碍。机器 学习有望有效解决这些问题，因为它可以建模复杂的非线性关系 在许多相互作用的因素中，不依赖建模假设以及最新进展 学习为各种临床预测应用带来了令人兴奋的结果。虽然机器 学习已应用于识别潜在的准备候选者，在探索机器学习时几乎没有做任何事情， 特别是先进的深度学习技术，以评估预测性因素，以保留预备护理和 有效的准备使用。 为了缩小知识的差距，拟议的研究旨在探索先进的机器学习技术 确定预备护理中保留的保护性和风险因素，并在关键人群中有效使用 泰国。我们将执行描述性统计分析，以表征MSM之间的PREP使用模式 TGW（AIM 1）;开发深度学习模型，以预测损失以跟进准备和有效的准备使用 （目标2）;并设计一个可解释的风险评分系统，用于识别中断和中断风险的客户 无效的准备使用，具有可解释的推理逻辑和相关的人口，行为，社会和 临床因素（目标3）。 这项研究对NIMH的艾滋病毒预防和战略目标的优先研究有反应3.2 定制现有干预措施以优化结果的策略。这项研究的发现和 基于预测模型的评分系统将告知量身定制的干预措施，以优化准备工作和 促进差异化的准备服务交付，粘贴了使用PREP的稳固基础，以预防精确的HIV 作为有效的策略。