在无家可归的年轻人中进行HIV预防的AI增强干预的临床试验
Clinical trial of an AI-augmented intervention for HIV prevention in youth experiencing homelessness
摘要：
无家可归的青年感染艾滋病毒的风险要大得多，他们缺乏稳定的住房，而且在无家可归的青年中，边缘化的种族、族裔和性别认同群体的青年比例过高，使情况更加复杂。健康公平的一个关键目标是改善这一人群的保护行为。一个很有希望的干预战略是从YEH人群中招募同龄人领袖，向他们的社会接触者推广使用避孕套和定期进行艾滋病毒检测等行为。这就提出了一个计算问题：应该选择哪些青年作为同龄人领袖，以最大限度地发挥干预措施的总体影响？我们开发了一个人工智能系统来优化社区健康环境中的社交网络干预。我们进行了一项临床试验，在美国一个大城市的救助中心招募了713名YEH。该临床试验将使用该算法计划的干预措施与年轻人社交网络中最高程度的节点被招募为同龄人领导者（公共卫生的标准方法）和仅观察的对照组进行了比较。临床试验的结果表明，AI组的年轻人在艾滋病毒传播的关键风险行为方面有统计学显着减少，而其他组的年轻人则没有。据我们所知，这是第一次实证验证使用人工智能方法来优化健康社交网络干预措施。最后，我们讨论了在项目过程中吸取的经验教训，这些经验教训可能会为未来在社区干预中使用人工智能提供信息。
英文摘要：
Youth experiencing homelessness (YEH) are subject to substantially greater risk of HIV infection, compounded both by their lack of access to stable housing and the disproportionate representation of youth of marginalized racial, ethnic, and gender identity groups among YEH. A key goal for health equity is to improve adoption of protective behaviors in this population. One promising strategy for intervention is to recruit peer leaders from the population of YEH to promote behaviors such as condom usage and regular HIV testing to their social contacts. This raises a computational question: which youth should be selected as peer leaders to maximize the overall impact of the intervention? We developed an artificial intelligence system to optimize such social network interventions in a community health setting. We conducted a clinical trial enrolling 713 YEH at drop-in centers in a large US city. The clinical trial compared interventions planned with the algorithm to those where the highest-degree nodes in the youths' social network were recruited as peer leaders (the standard method in public health) and to an observation-only control group. Results from the clinical trial show that youth in the AI group experience statistically significant reductions in key risk behaviors for HIV transmission, while those in the other groups do not. This provides, to our knowledge, the first empirical validation of the usage of AI methods to optimize social network interventions for health. We conclude by discussing lessons learned over the course of the project which may inform future attempts to use AI in community-level interventions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
