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（14-24 岁）死亡的主要原因，对非裔美国人的影响尤为严重 城市急诊室是预防暴力的重要机会，每年有超过 600,000 名青少年。 寻求暴力相关伤害的治疗 在我们对城市急诊室遭受暴力伤害的青少年的研究中，我们发现： 两年内，37% 的人因重复暴力伤害而返回，59% 的人经历过枪支暴力，38% 的人被捕， 尽管如此，减少急诊就诊后重复暴力的策略尚未得到充分研究。 鉴于我们的工作表明，单次急诊科干预措施可以有效减少低收入群体的暴力行为 对于风险青少年，这种疗法的应用范围扩大到解决多个问题的严重程度 会议并通过包括护理管理来加强，代表了改变改变的潜在有效方法 我们最近对这种方法（S-RTI）的试点效果很好。 收到的，解决了之前多届会议干预中发现的问题（例如交通），并添加了 虽然这种 S-RTI 方法具有创新性/前景，但它是资源密集型的，并且确实是一种远程治疗方法。 不解决治疗反应的异质性相反，适应性策略允许“及时”。 量身定制，在干预过多和干预不足之间提供平衡，并提高结果，同时 强化学习是一个人工智能领域，它允许计算机系统“学习”。 来自先前治疗的成功，是构建适应性“及时”治疗的一种有前途的方法 在本研究中，我们正在测试两种版本的 RTI，其中包括标准 RTI 条件 (S-RTI)。 单个 ED 会话，随后进行 5 个远程会话，以及经过优化的自适应 RTI 版本 (AI-RTI) 强化学习在三个级别之间提高/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。 研究目标是： 1) 改进/调整我们的 RTI 以使用两个包（S-RTI；AI-RTI）进行交付； 2) 进行 3- 手臂随机对照试验招募了 750 名寻求 ED 护理的遭受暴力伤害的青少年（年龄：14-24 岁），以比较 S-RTI 的疗效 (n=250)、AI-RTI (n=300) 和控制 (n=200)，以及， 3) 通过以下方式评估 AI-RTI RL 算法的适应性； 将前 50% 的参与者与后 50% 的参与者进行流程变量（例如，参与度）进行比较。 结果（6 个月、12 个月）包括攻击和受害，次要结果包括 ED 累犯。 目前的研究包括暴力伤害、药物滥用、心理健康症状和刑事司法参与。 有望提高社会群体之间的暴力发生率以及关键的健康差距 弱势青少年，临床试验遇到了来自新冠病毒的挑战。 补充行政资金的重点是增加临床招募地点（在当前招募地点的基础上）， 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，保留减少暴力对公共健康产生重大影响的潜力。