SCH: EXP: LifeRhythm: A Framework for Automatic and Pervasive Depression Screening Using Smartphones

由于其高患病率和重大的健康和经济影响，抑郁症是一个深刻的公共卫生问题。目前，抑郁症的筛查是基于医生管理的访谈工具或患者自我报告。虽然医生管理的工具更具权威性，但由于费用和缺乏训练有素的心理健康专业人员，可用性受到限制。另一方面，患者自我报告存在回忆偏差和患者参与不一致。特别是，这两种方法都不能令人满意地解决抑郁症的慢性和复发性，需要经常评估监测发病和进展。为了解决抑郁症作为一个公共卫生问题，迫切需要一个客观，准确，易于获得和可扩展的抑郁症筛查工具。智能手机在世界各地的普及为在大量人群中自动和普遍筛查抑郁症创造了新的机会。这项建议的教育计划包括发展和加强各种本科生和研究生课程，以及通过临床监督向医学生传播成果，并增加代表性不足的群体参与研究和外展活动。该项目的目标是开发LifeRhythm，这是一种使用智能手机数据进行自动和普遍抑郁症筛查的自动化系统。LifeRhythm通过智能手机持续监测个人的行为节奏，从原始数据中提取归一化特征，并应用多个机器学习模型进行实时诊断。该项目将LifeRhythm应用于两种具有互补优势的环境。第一种设置使用从智能手机收集的“高分辨率”传感数据，这些数据提供了极其丰富和描述性的行为数据，从而为机器学习模型提供了最佳利用。第二种设置使用从大规模WiFi网络被动收集的“低分辨率”无线关联元数据，这消除了在智能手机上收集数据的需要，并且对于大型组织来说特别有价值，在那里它可以以非常低的成本同时自动提供数万人的抑郁症筛查。LifeRhythm的开发将与几项密切相关的机器学习研究工作相结合，包括协作预测、综合学习、时间动态建模以及使用基于乘法权重的技术进行模型细化的新技术。虽然这项建议主要集中在筛查工具的开发上，但未来的工作自然会开发相关的干预计划。此外，这项研究可能会导致适用于其他情绪障碍，如双相情感障碍的方法。更广泛的影响将包括向技术界传播研究成果（和附加说明的数据集）。该项目的网站（http：//nlab.engr.uconn.edu/sch.html）提供关于研究和成果的更多信息。

Because of its high prevalence and significant health and economic impacts, depression is a profound public health problem. Currently, screening for depression is based on physician-administered interview tools or patient self-report. While physician-administered tools are more authoritative, availability is constrained both by cost and lack of access to trained mental health professionals. Patient self-reporting, on the other hand, suffers from recall bias and inconsistent patient participation. In particular, neither approach satisfactorily addresses the chronic and recurring nature of depression that requires frequent assessment for monitoring onset and progress. To address depression as a public health problem, there is urgent need for an objective, accurate, easily accessible and scalable depression screening tool. The ubiquitous adoption of smartphones around the world creates new opportunities in automatic and pervasive screening of depression across large populations. The education plan of this proposal includes developing and enhancing various undergraduate and graduate-level courses, as well as disseminating the results to medical students through clinical supervision and increasing the participation from under-represented groups in research and outreach activities. The goal of this project is to develop LifeRhythm, an automated system for automatic and pervasive depression screening using smartphone data. LifeRhythm continuously monitors the behavioral rhythms of individuals through their smartphones, extracts normalized features from the raw data, and applies multiple machine-learning models for real-time diagnosis. The project applies LifeRhythm to two settings that have complementary strengths. The first setting uses "high-resolution" sensing data collected from smartphones, which provides extremely rich and descriptive behavioral data, allowing the best leverage for machine learning models. The second setting uses "low-resolution" wireless association meta-data collected passively from large-scale WiFi networks, which eliminates the need of data collection on smartphones and can be especially valuable for a large organization, where it could automatically provide depression screening of tens of thousands of people simultaneously at very little cost. Development of LifeRhythm will be coupled with several tightly related machine-learning research efforts, including novel techniques for collaborative prediction, integrative learning, modeling of temporal dynamics, and model refinement using multiplicative-weights-based techniques. Though this proposal is primarily focused on development of screening tools, future work could naturally develop an associated intervention program. In addition, this research may lead to methodologies that are applicable to other mood disorders such as bipolar illness. The broader impacts will include dissemination of research results (and the annotated dataset) to the technical communities. The project web site (http://nlab.engr.uconn.edu/sch.html) provides access to additional information on research and results.