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

这项小企业创新研究（SBIR）二期项目的广泛影响和商业潜力将彻底改变自闭症患者的治疗方法。每68个美国儿童中就有一个患有自闭症（超过110万）。据估计，每年为那些可能受益的人提供应用行为分析（ABA）疗法的费用为75亿美元。社会影响包括：1)全球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并充分发挥其潜力；2)孩子适合治疗并接受治疗的家庭将减轻压力，改善家庭生活；3)没有有效治疗自闭症儿童的额外终身成本，大约是治疗成本的十倍，将会减少。由于将首次收集高质量、背景丰富的ABA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)可以识别自闭症个体的模式，从而更好地了解自闭症的变化，并针对这些差异创建治疗方法；b)扩展数据挖掘的前沿以提供实时指导将有助于ABA治疗内外的许多领域。拟议的项目将通过将基于代理的指导技术转化为主管和教师的适应性和智能ABA治疗助手，来优化自闭症患者的治疗效果。该项目在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方面突破了界限，这些指导和数据收集支持可以自然地集成到教学过程中，并且易于学习和使用。ABA治疗专家，主管和讲师将验证分析和结果指导纳入技术。可用性工程的先进理论，包括一些由项目团队开发的理论，将用于构建主管和教师无需学习新概念和语法就可以凭直觉判断的界面。该项目将利用从多个治疗接受者和多个治疗提供者的多次治疗中收集的日志来发现隐藏的模式，并帮助主管为自闭症患者选择合适的个性化治疗步骤。该项目将建立在可视化、时间预测建模的机器学习和顺序模式挖掘方面的大量近期工作基础上，包括项目团队之前的一些成果。将特别关注最近在教育数据挖掘和智能辅导方面的工作。

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.