SBIR Phase II: An Intelligent Mental Health Therapy System

这个小型企业创新研究第二阶段项目的更广泛的影响/商业潜力是帮助使治疗与患者的偏好、信念和价值观更加一致，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，但许多患者在获得充分好处之前就退出了，因为他们对治疗不满意或不投入。拟议的项目涉及收集在线治疗系统中所有患者行动的数据，以及他们对每项活动的评级和随着时间的推移他们的症状改善。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验，为治疗的最佳下一步提供建议。这就是智能咨询系统。它的工作原理非常类似于电影流媒体服务或在线图书销售商，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高级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，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，在形式、长度、速度和其他特征上有所不同。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，为了实现最大限度的遵守和降低流失率，该平台将启用个性化的激励干预和支持性信息。传递时间和支持性信息的内容将根据预计的治疗进展而调整和变化。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进行增量训练，因此它将受益于随着时间的推移精度的提高。我们将在多个时间分辨率上提取局部、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对患者的治疗成功做出贡献，并预测用户是改善还是恶化。这将导致自适应的激励信息和建议，根据重要的已确定的治疗特征进行量身定制治疗。

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.