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在乌干达艾滋病毒感染者青少年中发现问题性物质使用的预测因素:机器学习方法

Claire Najjuuko^1,2,3, Rachel Brathwaite⁴, Massy Mutumba⁵

¹International Center for Child Health and Development, Brown School, Washington University in St. Louis, St. Louis, MO, 63130, USA.

AIDS and behavior

|September 18, 2025

概括

机器学习模型可以预测乌干达艾滋病毒感染的年轻人的问题性物质使用 (PSU). 关键的风险因素包括心理健康,收入和社会影响,指导有针对性的干预措施.

科学领域:

公共卫生公共卫生
机器学习机器学习
艾滋病毒研究艾滋病毒研究

背景情况:

年轻人吸毒是一个主要的公共卫生问题,特别是在撒哈拉以南非洲 (SSA).
问题性物质使用 (PSU) 加剧了艾滋病毒的传播,并阻碍了艾滋病毒感染者青年 (YLHIV) 参与艾滋病毒护理.

研究的目的:

开发和比较机器学习 (ML) 模型来预测乌干达的YLHIV中PSU.
在这个人群中识别与PSU相关的多层次风险因素.

主要方法:

分析了乌干达200名YLHIV (18-24岁) 的横截面数据集.
经过10倍交叉验证,训练和评估了6个ML模型,其性能由AUROC和AUPRC测量.
进行了特征重要性分析,以确定PSU的关键预测因素.

主要成果:

随机森林模型表现出最好的预测性能,AUROC为0.78和AUPRC为0.75.
显著的PSU预测因素包括抑郁症,性冒险,收入,不良童年经历,家庭/朋友的影响,社区活动,家庭规模,以及对酒精对艾滋病毒治疗的影响的知识.
预测者跨越个人,人际关系和社区层面.

结论:

ML模型对预测YLHIV中PSU充满希望,有助于早期识别和干预.
了解多层次风险因素对于制定有针对性的策略来缓解PSU和改善艾滋病毒管理结果至关重要.

关键词:

预测建模的预测建模.撒哈拉以南非洲地区.使用物质使用物质.感染艾滋病毒的青少年

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