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

使用强化学习自动调整远程治疗干预 (RTI)，以减少青少年暴力参与

• 批准号: 10834339
• 负责人: Patrick M. Carter
• 金额: $ 49.69万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2024-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10834339
• 关键词: Accident and Emergency department; Address; Administrative Supplement; Adolescent; Affect; African American; African American population; Aggressive behavior; Algorithms; Artificial Intelligence; Behavior Therapy; Cause of Death; Clinical; Clinical Trials; Computer Systems; Criminal Justice; Disadvantaged; Electronics; Emergency Care; Emergency department visit; Enrollment; Equilibrium; Funding; Goals; Health Services Accessibility; Heterogeneity; Homicide; Injury; Intervention; Learning; Managed Care; Mental Health; Outcome; Population; Process; Psychological reinforcement; Public Health; Remote session; Research; Resources; Risk; Sampling; Severities; Site; Surveys; Symptoms; Telephone; Testing; Text Messaging; Therapeutic Intervention; Time; Transportation; Urban Community; Victimization; Violence; Violent injury; Work; Youth; access disparities; arm; artificial intelligence algorithm; assault; comparative efficacy; coronavirus disease; cost; efficacious intervention; experience; gun violence; health disparity; high risk; innovation; intervention delivery; patient response; preservation; primary outcome; recidivism; recruit; remote therapy; secondary outcome; stem; substance use; success; treatment response; violence prevention

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.

凶杀是青少年死亡的主要原因（年龄：14-24），对非裔美国人的影响不成比例 人群。城市EDS是预防暴力的关键机会，每年> 60万名青少年 寻求治疗与暴力有关的伤害。在我们对城市ED中遭受巨大伤害的青年的研究中，我们发现 在2年内，有37％的人因反复暴力伤害而返回，有59％的枪支暴力，38％被捕， 1％的人死亡。尽管如此，在ED访问后减少重复暴力行为的策略还没有很好地研究。 鉴于我们的工作证明了单一会议的干预措施有效地减少了较低的暴力 风险青少年（这种疗法的应用）扩展了，以解决多个的问题严重程度更大 会议并通过包括护理管理来增强，代表了一种可能更有效的方法 高风险遭受损害的青少年的风险轨迹。我们最近的这种方法的飞行员（S-RTI）很好 收到，解决了先前的多期干预措施（例如，运输）中发现的问题 远程治疗（例如电话）。虽然创新/有前途，但这种S-RTI方法是资源密集的，并且确实如此 不能解决治疗反应中的异质性。相比之下，自适应策略允许“及时” 裁缝在太多和没有足够的干预措施之间提供平衡，并提高结果 降低成本。强化学习是一个人工智能领域，允许计算机系统“学习” 从先前治疗的成功来看，是一种建立适应性“及时”的有前途的方法 干预措施。在这项研究中，我们正在测试两个版本的RTI，标准RTI条件（S-RTI）完成了 一个单一的ED会话，然后进行5个远程会话，以及由Audaptive RTI版本（AI-RTI） 强化学习以在三个层次之间加强/降低治疗强度（即远程治疗， 电子机器人消息传递，仅评估）基于患者对每日调查评估的反应。原始 研究目的是：1）使用两个软件包（S-RTI; AI-RTI）来完善/调整RTI以进行递送； 2）进行3- ARM RCT注册了750个遭受巨大伤害的青少年，以寻求ED护理（年龄：14-24）以比较S-RTI的效率 （n = 250），ai-rti（n = 300）和对照（n = 200）; 3）评估AI-RTI RL算法的适应性 将注册的前50％与过程变量的第二个50％（例如参与度）进行比较。基本的 成果（6个月，12个月）包括侵略性和胜利。次要结果包括ED累犯 暴力伤害，吸毒，心理健康症状和刑事司法参与。而当前的研究 有望解决社会中的暴力率提高以及关键的健康差异的承诺 处境不利的年轻人，临床试验遇到了库维德（Covid）面临的挑战。此请求 补充行政基金专注于添加一个临床招聘站点（在目前注册的网站上）， 以及临床和研究人员，避免降低科学范围，并增强项目的能力 实现原始研究的目标/目标，保留了高公共卫生影响减少暴力的潜力。