SBIR Phase II: An Intelligent Mental Health Therapy System

SBIR第二期：智能心理健康治疗系统

• 批准号: 1631871
• 负责人: Sherry Benton
• 金额: $ 70.9万
• 依托单位: TAO Connect, Inc.
• 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: 2016-10-01 至 2021-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1631871&HistoricalAwards=false
• 关键词: SBIR; Phase; II; Intelligent; Mental

The broader impact/commercial potential of this Small Business Innovation Research, Phase II project is to help make therapy more consistent with patient preferences, beliefs, and values to maximize engagement in therapy and improve patient outcomes. Therapy for mental health problems is highly effective, yet many patients drop out before getting the full benefit because they are not satisfied or engaged in the therapy. The proposed project involves collecting data on all of patients actions in the online treatment system along with their ratings of each activity and their symptom improvement over time. The research and development team will use this data to create a machine learning system that will make suggestions for best next steps in therapy based on what thousands of other users experienced. This is the intelligent counseling system. It will work very similarly to movie streaming services or online book sellers who recommend movies or books to you based on your past preferences and the preferences of thousands of other users.The proposed project will develop a feedback and recommendation system based on advanced analytics and machine learning techniques to provide personalized treatments to customize and individualize online mental health treatment, the Intelligent Counseling System (ICS). This personalized system will contain a number of alternative treatment items from several theoretical perspectives, using a variety of patient interactive activities, varying in format, length, pace, and other characteristics. In such a setting, a recommendation system can predict the users' preferences and recommend the subsequent treatment component. In addition, to achieve maximum adherence and to decrease the attrition rate, the platform will enable personalized motivational interventions and supportive messaging. The delivery times and the content of supportive messaging will adapt and vary depending on the projected treatment progress. Our machine learning based system will be trained incrementally as more data becomes available over time, thus it will benefit from improved accuracy over time. We will extract local, semi-local, and global temporal features at multiple temporal resolutions and will use feature selection techniques to identify which factors contribute to the success of treatments for patients, and to predict if a user is improving or is deteriorating. This will result in adaptive motivational messages and recommendation for tailoring treatment in term of important identified treatment features.

这个小型企业创新研究的更广泛的影响/商业潜力，第二阶段项目是帮助使治疗更符合患者的偏好，信仰和价值观，以最大限度地参与治疗和改善患者的结果。心理健康问题的治疗非常有效，但许多患者在获得全部益处之前就退出了，因为他们不满意或不参与治疗。拟议的项目涉及收集数据的所有患者的行动，在网上治疗系统沿着与他们的评级，每一个活动和他们的症状改善随着时间的推移。研究和开发团队将使用这些数据创建一个机器学习系统，该系统将根据数千名其他用户的体验为治疗的最佳后续步骤提供建议。这就是智能咨询系统。它的工作原理与电影流媒体服务或在线图书销售商非常相似，他们会根据您过去的偏好和数千名其他用户的偏好向您推荐电影或书籍。拟议的项目将开发一个基于高级分析和机器学习技术的反馈和推荐系统，提供个性化的治疗，以定制和个性化的在线心理健康治疗，智能咨询系统（ICS）。该个性化系统将包含来自多个理论视角的多个替代治疗项目，使用各种患者交互活动，在格式、长度、速度和其他特征方面有所不同。在这样的设置中，推荐系统可以预测用户的偏好并推荐后续治疗组件。此外，为了实现最大程度的遵守和降低流失率，该平台将能够进行个性化的激励干预和支持性信息传递。支持性信息的传递时间和内容将根据预计的治疗进展进行调整和变化。我们的基于机器学习的系统将随着时间的推移获得更多的数据而逐渐训练，因此它将随着时间的推移从提高的准确性中受益。我们将在多个时间分辨率下提取局部，半局部和全局时间特征，并将使用特征选择技术来识别哪些因素有助于患者治疗的成功，并预测用户是否正在改善或恶化。这将产生自适应激励信息和建议，以便根据重要的已识别治疗特征定制治疗。