SBIR Phase II: Using Data Mining to Optimally Customize Therapy for Individuals with Autism

SBIR 第二阶段：利用数据挖掘为自闭症患者优化定制治疗

• 批准号: 1632257
• 负责人: John Nosek
• 金额: $ 73.22万
• 依托单位: Guiding Technologies Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-08-01 至 2021-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1632257&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Using; Data

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase II project will revolutionize the treatment of individuals with autism. One of every sixty-eight US children has autism (over 1.1 million). The estimated cost of providing Applied Behavior Analysis (ABA) therapy to those who could benefit is $7.5 billion dollars annually. Societal impacts include: 1) more individuals with autism across the globe will receive treatment regimens that will enable them to live more fulfilled lives and reach their full potential; 2) families whose children are good candidates for treatment and receive it will experience reduced stress and better family life; and 3) the additional lifetime cost of not effectively treating children with autism, which is approximately ten-fold the cost of treatment, will be reduced. Because high-quality, contextually rich ABA performance data will be collected for the first time, efforts to apply data analytics will contribute in two important ways: a) patterns may be discerned across individuals with autism to better understand variations in autism and create therapies to target these differences; b) expansion of the frontiers of data mining to provide guidance in real time will contribute to a number of areas within and beyond ABA therapy.The proposed project will optimize therapy outcomes for individuals with autism by transforming agent-based guiding technology into an adaptive and intelligent ABA therapy assistant for supervisors and instructors. The project pushes the boundaries in providing cost-effective, adaptable, intelligent, real-time guidance and data-collection support to instructors that integrates naturally into the instructional process and is easy to learn and use. ABA therapy experts, supervisors and instructors will verify the analyses and resulting guidance incorporated into the technology. Advanced theories of usability engineering, including some developed by the project team, will be used to build interfaces that supervisors and instructors can intuit without the need for learning new concepts and syntax. The project will utilize the collected logs from multiple sessions with multiple therapy recipients and multiple therapy providers to uncover hidden patterns and assist supervisors in selecting appropriate therapy steps personalized for the individual with autism. The project will build on a large body of recent work in visualization, machine learning on temporal predictive modeling and sequential pattern mining, including some of the previous results of the project team. Special attention will be paid to the recent work in educational data mining and intelligent tutoring.

这个小企业创新研究（SBIR）第二阶段项目的更广泛的影响/商业潜力将彻底改变自闭症患者的治疗。每68名美国儿童中就有一名患有自闭症（超过110万）。为那些可能受益的人提供应用行为分析（阿坝）治疗的估计成本为每年75亿美元。社会影响包括：（1）地球仪上更多的自闭症患者将接受治疗方案，使他们能够过上更充实的生活，充分发挥其潜力;（2）儿童适合接受治疗并接受治疗的家庭将减轻压力，改善家庭生活;以及3）未有效治疗自闭症儿童的额外终生成本（约为治疗成本的十倍）将减少。由于将首次收集高质量，上下文丰富的阿坝表现数据，因此应用数据分析的努力将在两个重要方面做出贡献：a）可以在自闭症患者中识别模式，以更好地了解自闭症的变化并创建针对这些差异的治疗方法; B）扩展数据挖掘的前沿，以提供真实的时间指导，将有助于阿坝治疗内外的许多领域。拟议的项目将优化治疗结果，通过将基于代理的指导技术转化为自适应和智能的阿坝治疗助手，为自闭症患者提供指导。该项目推动了为教师提供具有成本效益、适应性强、智能、实时指导和数据收集支持的界限，这些指导和数据收集支持自然融入教学过程，易于学习和使用。阿坝治疗专家，主管和讲师将验证分析和所产生的指导纳入技术。 可用性工程的先进理论，包括项目团队开发的一些理论，将用于构建主管和教师可以凭直觉而无需学习新概念和语法的界面。该项目将利用从多个治疗接受者和多个治疗提供者的多个会话中收集的日志，以发现隐藏的模式，并协助主管为自闭症患者选择适当的个性化治疗步骤。该项目将建立在可视化、时间预测建模和序列模式挖掘的机器学习方面的大量近期工作的基础上，包括项目团队以前的一些成果。特别关注教育数据挖掘和智能辅导方面的最新工作。