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: 10611439
• 负责人: Patrick M. Carter
• 金额: $ 58.64万
• 依托单位: UNIVERSITY OF MICHIGAN AT ANN ARBOR
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-04-15 至 2025-03-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10611439
• 关键词: 20 year old; Accident and Emergency department; Address; Admission activity; Adolescent; Affect; African American; African American population; Aggressive behavior; Artificial Intelligence; Behavior; Behavior Therapy; Caucasians; Cause of Death; Clinical; Communities; Computational algorithm; Computer Systems; Criminal Justice; Decision Making; Development; Disadvantaged; Disparity; Dose; Emergency Care; Emergency department visit; Enrollment; Environment; Equilibrium; Event; Feedback; Future; Health; Health Resources; Health Services Accessibility; Heterogeneity; Homicide; Hospitals; Imprisonment; Injury; Intervention; Learning; Link; Longitudinal Studies; Managed Care; Managed Care Programs; Measures; Mental Health; Modality; Outcome; Outcome Assessment; Participant; Patients; Pattern; Performance; Persons; Population; Process; Psychological reinforcement; Public Health; Remote session; Resources; Risk; Risk Behaviors; Sampling; Services; Severities; Standardization; Statistical Models; Symptoms; Telephone; Testing; Text Messaging; Therapeutic Intervention; Time; Transportation; Treatment Efficacy; Treatment Protocols; Urban Community; Variant; Victimization; Violence; Violent injury; Work; Youth; access disparities; arm; artificial intelligence algorithm; automated text message; comparative efficacy; cost; efficacious intervention; experience; gun violence; high risk; improved; inattention; innovation; intervention delivery; learning algorithm; minority children; neighborhood disadvantage; outcome disparities; patient response; personalized medicine; preservation; primary outcome; protective factors; recidivism; remote therapy; secondary outcome; societal costs; socioeconomic disadvantage; substance use; success; telephone delivery; theories; treatment response; treatment strategy; two way texting; video chat; violence prevention; youth violence

Youth violence is a key public health problem. Homicide is a leading cause of death among adolescents (age:14- 20) and disproportionately impacts African-American populations. Urban EDs are a critical opportunity for violence prevention, especially with >600,000 adolescents/year seeking treatment for violence-related injuries. In our longitudinal study of violently-injured adolescents 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 the importance of the problem, strategies to decrease repeat violence after an ED visit have not been well studied. Given our prior work demonstrating that theoretically-based single session ED interventions are efficacious reducing violence among 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 and addressed problems identified in prior multisession interventions (e.g., transportation) with the addition of remote therapy delivery (e.g., phone). While innovative and promising, this S-RTI approach is resource intensive and does not address heterogeneity in treatment responses. By contrast, adaptive treatment strategies allow for “just-in-time” tailoring that provides a balance between too much and not enough intervention and enhances outcomes while reducing the use of costly resources. 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 propose to test two versions of our RTI, a standard RTI condition (S-RTI) comprised of a single ED session followed by 8 remote therapy sessions, and an adaptive RTI version (AI-RTI) optimized by reinforcement learning to step up or down the intensity of treatment between three levels (i.e., remote therapy sessions, automated two- way text messaging, assessment only) based on patient response to daily text message assessments. The specific aims are: 1) To refine and adapt our RTI for delivery using two packages (S-RTI; AI-RTI); 2) To conduct a 3-arm RCT enrolling 900 violently-injured adolescents seeking ED care (age:14-20) to compare the efficacy of S-RTI (n=300), AI-RTI (n=400), and a control condition (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, helpfulness/likability). Primary outcomes (assessed at 4-, 8-, and 12-months) include aggression, victimization, and ED recidivism for violent injury. Secondary outcomes include substance use, mental health symptoms, and criminal justice involvement. As a secondary aim, we will compare resource utilization (i.e., costs/event averted) for the active intervention conditions. Given elevated rates of violence among socio-disadvantaged youth with disparities in access to services, the proposed study has the potential for significant public health impact.

青年暴力是一个关键的公共卫生问题。凶杀是青少年死亡的主要原因（年龄：14-- 20）并不成比例地影响非裔美国人人口。城市EDS是一个关键的机会 预防暴力，尤其是> 60万名青少年/年，寻求与暴力有关的伤害治疗。 在我们对城市ED中遭受巨大损害的青少年的纵向研究中，我们发现在2年内，有37％的人返回 对于反复的暴力伤害，有59％的人遭受了枪支暴力，38％被捕，1％的人死亡。尽管有 问题的重要性，降低ED访问后重复暴力行为的策略不是很好的研究。 鉴于我们先前的工作证明了基于理论的单个会话ED干预措施有效 减少较低风险青少年的暴力行为，这种疗法的应用已扩展，以解决更大的问题 在多个会议上的问题严重程度，并通过包括护理管理（包括护理管理）来增强，这代表了潜在的 更改高风险遭受损害的青少年风险轨迹的简单方法。我们最近的飞行员 这种方法（S-RTI）受到了良好的接收，并解决了先前多期干预中发现的问题 （例如，运输）加上远程治疗（例如电话）。虽然创新和有前途，但 这种S-RTI方法是资源密集的，并且不能解决治疗反应中的异质性。经过 对比，自适应治疗策略允许“及时”裁缝，从而在太多之间达到平衡 而且没有足够的干预和增强结果，同时减少了昂贵的资源的使用。加强 学习是一个人工智能领域，允许计算机系统从先验的成功中“学习” 治疗方法是建立自适应“及时”干预措施的承诺方法。对于这项研究，我们 提案测试我们的RTI的两个版本，一个标准的RTI条件（S-RTI）完成了一次ED会话 然后进行8次远程治疗课程，以及通过增强学习优化的自适应RTI版本（AI-RTI） 在三个级别（即远程治疗课程中，自动化的两个级别）之间的治疗强度上升或降低 基于患者对每日短信评估评估的患者反应，仅进行文本消息传递，仅评估）。这 具体目的是：1）使用两个软件包（S-RTI； AI-RTI）来完善和调整我们的RTI进行交付； 2）进行 3臂RCT注册了900名遭受巨大伤害的青少年（年龄：14-20），以比较 S-RTI（n = 300），AI-RTI（n = 400）和控制条件（n = 200）; 3）评估AI-RTI的适应性 通过将注册的前50％与过程变量的第二个50％进行比较（例如，参与度， 有益的/可爱的）。主要结果（在4-、8和12个月时评估）包括侵略性，胜利， 和埃德（Ed）对暴力伤害的累犯。次要结果包括吸毒，心理健康症状和 刑事司法参与。作为次要目的，我们将比较资源利用率（即避免成本/事件） 对于主动干预条件。鉴于社会待遇的年轻人的暴力发生率升高 拟议的研究有可能产生重大的公共卫生影响。