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

SCH：EXP：LifeRhythm：使用智能手机进行自动和普遍抑郁症筛查的框架

• 批准号: 1407205
• 负责人: Bing Wang
• 金额: $ 71.88万
• 依托单位: University of Connecticut
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1407205&HistoricalAwards=false
• 关键词: SCH; EXP; LifeRhythm; Framework; Automatic

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.

由于抑郁症是一个严重的公共卫生问题，由于其高龄和重大的健康和经济影响。目前，抑郁症的筛查是基于医师管理的访谈工具或患者自我报告的。尽管医师管理的工具更具有权威性，但由于成本和缺乏训练的心理健康专业人员的访问，可用性受到限制。另一方面，患者自我报告会遭受召回偏见和患者参与不一致的影响。特别是，两种方法都令人满意地解决了抑郁症的慢性和经常性性质，这需要经常评估以监测和进步。为了解决抑郁症作为公共卫生问题，迫切需要使用客观，准确，易于访问和可扩展的抑郁症筛查工具。无处不在的世界各地智能手机的采用在自动和普遍筛查抑郁症的人群中创造了新的机会。该提案的教育计划包括开发和增强各种本科和研究生水平的课程，以及通过临床监督向医学生传播结果，并增加了代表性不足的研究和外展活动的参与。该项目的目的是开发Liferanthm，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。 LiferAlthm通过智能手机连续监测个体的行为节奏，从原始数据中提取标准化功能，并应用多个机器学习模型进行实时诊断。该项目将LiferAlthm应用于具有互补优势的两个环境。第一个设置使用从智能手机收集的“高分辨率”传感数据，该数据提供了极为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的杠杆作用。第二个设置使用从大规模WiFi网络被动收集的“低分辨率”无线结合元数据，这消除了智能手机上数据收集的需求，对于大型组织而言可能特别有价值，在大型组织中，它可以自动提供抑郁症筛查数以万计的人，几乎没有成本。 LiferAlthm的开发将与几项紧密相关的机器学习研究工作相结合，包括用于协作预测的新技术，综合学习，时间动力学的建模以及使用基于乘法的技术的模型改进。尽管该提案主要集中在开发筛查工具上，但未来的工作自然可以制定相关的干预计划。此外，这项研究可能导致适用于其他情绪障碍（例如双极疾病）的方法。更广泛的影响将包括向技术社区传播研究结果（以及带注释的数据集）。项目网站（http://nlab.engr.uconn.edu/sch.html）提供了有关研究和结果的其他信息。