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

这个小企业创新研究（SBIR）第一阶段项目的更广泛影响/商业潜力包括数据挖掘和自闭症治疗方面的创新。应用行为分析（ABA）疗法是治疗自闭症的金标准。将数据分析应用于ABA治疗过程的数据将在几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)模式可以与其他数据匹配，例如基因组数据，以确定交叉模式，这可能有助于更好地了解自闭症和改进治疗方法；c)数据挖掘的前沿将得到扩展，以提供实时指导。该项目将产生以下社会影响：1)全球更多的自闭症患者将获得早期、高质量、低成本的治疗方案，使他们能够过上更充实的生活，充分发挥他们的潜力；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。该项目旨在从单个学生的试验序列中提取信息序列模式，利用它们准确预测试验结果，并利用预测模型为如何修改试验和学生训练步骤提供个性化建议。为了实现这一目标，将使用预测数据挖掘。为了开发准确的预测模型，该项目将基于机器学习在时间预测建模和顺序模式挖掘方面的大量最新工作，包括项目团队之前的一些结果。将特别关注最近在教育数据挖掘和智能辅导方面的工作。具体的关键目标包括：1)预测建模试验数据的表示：如何以最适合预测建模的方式表示原始序列数据；2)试验结果预测模型的建立：哪个模型最适合序贯试验的结果预测，如何从高维的多治疗受体序贯数据中训练预测模型；3)基于早期分类模型的自闭症儿童的指导治疗：如何调整和扩展项目组先前开发的方法来指导试验。

The broader impact/commercial potential of this Small Business Innovation Research (SBIR) Phase I project includes innovations in data mining and the treatment of autism. Applied Behavior Analysis (ABA) therapy is the gold standard in treating autism. Applying data analytics to data from ABA therapy sessions will contribute in several 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) patterns may be matched with other data, such as genomic data, to identify cross-patterns that may be useful in better understanding autism and ways to improve therapy; and c) the frontiers of data mining will be expanded to provide guidance in real time. This project will have the following societal impacts: 1) many more individuals with autism across the globe will receive early, quality, cost-effective 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.The proposed project is to extract informative sequential patterns from trial sequences of an individual student, use them to accurately predict trial outcomes, and utilize the predictive model to provide individualized recommendations about how to modify trials and steps of student training. To achieve this goal, predictive data mining will be used. To develop accurate predictive models, the project will build on a large body of recent work in 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. Specific key objectives include: 1) Representation of Trial Data for Predictive Modeling: how to represent the raw sequential data in a way that is most suitable for prediction modeling; 2) Development of Models for Prediction of Trial Outcomes: which model is the most suitable for prediction of outcomes in sequential trials and how to train a prediction model from highly-dimensional multi-therapy recipient sequential data; and 3) Guiding Therapy of a Child with Autism Based on an Early Classification Model: how to adjust and extend the previously developed approach by the project team to guide trials.