Using Re-inforcement Learning to Automatically Adapt a Remote Therapy Intervention (RTI) for Reducing Adolescent Violence Involvement

杀人罪是青少年(14-24岁)死亡的主要原因，对非裔美国人的影响不成比例 人口。城市急诊室是预防暴力的关键机会，每年有60万青少年 寻求与暴力有关的伤害的治疗。在我们对城市急救中心暴力伤害青年的研究中，我们发现 在两年内，37%的人因反复遭受暴力伤害而回国，59%的人经历过枪支暴力，38%的人被捕， 1%的人死亡。尽管如此，减少在急诊室访问后再次发生暴力的策略还没有得到很好的研究。 鉴于我们的工作表明，单次教育干预是有效的，在较低的 风险青少年，这种疗法的应用范围扩大到解决更严重的问题 课程，并通过包括护理管理进行增强，是一种潜在有效的改变方法 暴力伤害高危青少年的危险轨迹。我们最近对这种方法的试点(S-RTI)很好 已收到，解决了在先前的多个会议干预(例如，交通)中发现的问题 远程治疗(例如，电话)。虽然有创新/前景，但S-RTI方法是资源密集型的，并且 没有解决治疗反应的异质性问题。相比之下，自适应策略允许“及时” 量身定做，在干预过多和干预不足之间取得平衡，同时提高结果 降低成本。强化学习是一种人工智能领域，它允许计算机系统“学习” 这是一种很有前途的构建适应性“及时”的方法 干预措施。在这项研究中，我们测试了RTI的两个版本，标准RTI条件(S-RTI)包括 一次ED会话和5次远程会话，以及经过优化的自适应RTI版本(AI-RTI 强化学习以提高/降低三个级别之间的治疗强度(即，远程治疗， 电子BOT消息，仅限评估)基于患者对每日调查评估的反应。原版 研究目标是：1)改进/调整我们的RTI以使用两个包(S-RTI；AI-RTI)交付；2)进行3- ARM随机对照试验招募750名寻求急救的暴力伤害青少年(年龄：14-24岁)以比较S-RTI的疗效 (n=250)、AI-RTI(n=300)和对照(n=200)；以及，3)通过以下方法评估AI-RTI RL算法的适应性 比较前50%的参与者和后50%的参与者在流程变量(例如参与度)上的差异。主要 结果(6个月、12个月)包括攻击和受害。次要结果包括勃起功能障碍累犯 暴力伤害、药物使用、精神健康症状和参与刑事司法。虽然目前的研究 有望解决暴力发生率上升的问题，以及关键的健康差距， 对于处境不利的青年，临床试验经历了来自新冠肺炎的挑战。此请求用于 补充行政资金的重点是增加一个临床招聘网站(在目前招募的网站上)， 以及临床和研究人员，以避免缩小科学范围，并增强项目的能力 实现最初的研究目的/目标，保持对公共健康的高度影响，减少暴力的潜力。

Homicide is a leading cause of death for adolescents (age:14-24), disproportionately impacting African-American populations. Urban EDs are a critical opportunity for violence prevention, with >600,000 adolescents/year seeking treatment for violence-related injuries. In our study of violently-injured youth in urban EDs, we found that within 2-years, 37% returned for a repeat violent injury, 59% experienced firearm violence, 38% were arrested, and 1% died. Despite this, strategies to decrease repeat violence after an ED visit have not been well studied. Given our work demonstrating that single session ED interventions are efficacious reducing violence in lower risk adolescents, the application of this therapy, expanded to address greater problem severity over multiple sessions and enhanced by including care management, represents a potentially efficacious approach for altering risk trajectories of higher-risk violently-injured adolescents. Our recent pilot of this approach (S-RTI) was well received, addressing problems identified in prior multisession interventions (e.g., transportation) with the addition of remote therapy (e.g., phone). While innovative/promising, this S-RTI approach is resource intensive and does not address heterogeneity in treatment responses. By contrast, adaptive strategies allow for “just-in-time” tailoring that provides a balance between too much and not enough intervention and enhances outcomes while reducing cost. Reinforcement learning is an artificial intelligence domain that allows computer systems to “learn” from the success of prior treatments and is a promising approach to constructing adaptive “just-in-time” interventions. For this study, we are testing two versions of an RTI, a standard RTI condition (S-RTI) comprised of a single ED session followed by 5 remote sessions, and an adaptive RTI version (AI-RTI) optimized by reinforcement learning to step up/down the intensity of treatment between three levels (i.e., remote therapy, electronic bot messaging, assessment only) based on patient response to daily survey assessments. The original study aims were: 1) To refine/adapt our RTI for delivery using two packages (S-RTI; AI-RTI); 2) To conduct a 3- arm RCT enrolling 750 violently-injured adolescents seeking ED care (age:14-24) to compare efficacy of S-RTI (n=250), AI-RTI (n=300), and control (n=200); and, 3) To evaluate adaptability of the AI-RTI RL algorithm by comparing the first 50% of enrollees to the second 50% on process variables (e.g., engagement). Primary outcomes (6-, 12-months) include aggression and victimization. Secondary outcomes include ED recidivism for violent injury, substance use, mental health symptoms, and criminal justice involvement. While the current study holds promise for addressing elevated rates of violence, as well as key health disparities, among socio- disadvantaged youth, the clinical trial has experienced challenges stemming from COVID. This request for supplemental administrative funds is focused on adding a clinical recruitment site (to the currently enrolling sites), as well as clinical and research staff to avoid reducing scientific scope and to enhance the project’s ability to achieve the original study aims/goals, preserving the potential for high public health impact reducing violence.