SBIR Phase II: An Intelligent Mental Health Therapy System

这个小企业创新研究二期项目的更广泛的影响/商业潜力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并改善患者的结果。心理健康问题的治疗非常有效，但许多患者在得到充分的好处之前就退出了，因为他们不满意或不参与治疗。拟议的项目包括收集所有患者在在线治疗系统中的行为数据，以及他们对每项活动的评分和他们随着时间的推移症状的改善。研发团队将利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经历，为下一步治疗提供最佳建议。这是智能咨询系统。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，后者根据你过去的偏好和成千上万其他用户的偏好向你推荐电影或书籍。拟议的项目将开发一个基于先进分析和机器学习技术的反馈和推荐系统，以提供个性化治疗，以定制和个性化在线心理健康治疗，即智能咨询系统（ICS）。这个个性化的系统将从几个理论角度包含许多替代治疗项目，使用各种患者互动活动，在格式、长度、速度和其他特征上各不相同。在这种情况下，推荐系统可以预测用户的偏好，并推荐后续的治疗组件。此外，为了达到最大程度的坚持并降低流失率，该平台将实现个性化的动机干预和支持性信息。提供支持信息的时间和内容将根据预计的治疗进展而调整和变化。随着时间的推移，随着可用数据的增加，我们基于机器学习的系统将逐渐得到训练，因此随着时间的推移，它将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确定哪些因素有助于患者治疗的成功，并预测用户是在改善还是在恶化。这将产生自适应的激励信息，并根据重要的已确定的治疗特征提出定制治疗的建议。

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.