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使用机器学习来预测艾滋病毒感染者在低资源环境中生活的青少年对抗逆转录病毒疗法的不良坚持

Claire Najjuuko^1,2, Rachel Brathwaite^3,2, Ziqi Xu⁴

¹Division of Computational and Data Science, Washington University in St. Louis, MO, USA.

AIDS (London, England)

|February 25, 2025

概括

机器学习模型可以预测艾滋病毒 (ALWHIV) 感染青少年抗逆转录病毒疗法 (ART) 坚持不良的情况. 关键的风险因素包括贫困,护理人员关系和入学率,使得有针对性的干预措施成为可能.

科学领域:

公共卫生公共卫生
传染性疾病传染性疾病
青少年健康青少年健康

背景情况:

抗逆转录病毒疗法 (ART) 的坚持和病毒抑制对于到2030年实现UNAIDS 95-95-95目标至关重要.
最佳的依从仍然是一个挑战,特别是在艾滋病毒感染青少年 (ALWHIV).

研究的目的:

开发一种预测模型,用于识别高风险的ALWHIV患者的ART依从性较差.
为了确定关键的社会行为风险因素,与贫困的ART坚持在这个人群中.

主要方法:

利用机器学习 (ML) 来分析复杂的,多维数据从一个6年的纵向随机对照试验 (RCT) 涉及635ALWHIV在乌干达.
评估了6个ML模型,选择基于AUROC和AUPRC的随机森林模型.
确定了导致ART依守不良的主要风险因素.

主要成果:

随机森林模型展示了预测能力,AUROC为0.71和AUPRC为0.55.
确定的主要风险因素包括先前的执法史,贫困,护理人员关系,自我概念,储蓄信心,ART持续时间,与护理人员的沟通频率,家庭规模,经济状况和入学率.

结论:

机器学习和社会行为数据显示,对预测ALWHIV的ART坚持不良有前途.
预测工具可以促进针对性干预的高风险个体的早期识别.
通过整合HIV表型和临床数据,可以进一步提高准确性.

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