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.

由于抑郁症的高患病率和重大的健康和经济影响，它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，但可获得性受到成本和缺乏训练有素的精神卫生专业人员的限制。另一方面，患者自我报告存在回忆偏差和不一致的患者参与。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，这需要经常评估以监测发病和进展。为了将抑郁症作为一个公共卫生问题加以解决，迫切需要一种客观、准确、易于获取和可扩展的抑郁症筛查工具。智能手机在世界各地的普及，为大规模人群的抑郁症自动和普遍筛查创造了新的机会。该提案的教育计划包括发展和加强各种本科和研究生水平的课程，以及通过临床监督向医科学生传播结果，并增加代表性不足群体参与研究和外联活动。该项目的目标是开发LifeRhythm，这是一个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。liferhym通过智能手机持续监测个人的行为节律，从原始数据中提取标准化特征，并应用多种机器学习模型进行实时诊断。该项目将liferrhythm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，提供极其丰富和描述性的行为数据，为机器学习模型提供最佳杠杆。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，对于大型组织来说尤其有价值，它可以以极低的成本自动同时为数万人提供抑郁症筛查。liferhym的开发将与几个紧密相关的机器学习研究工作相结合，包括协作预测、综合学习、时间动态建模的新技术，以及使用基于乘法权的技术进行模型改进。虽然这一建议主要侧重于筛选工具的开发，但未来的工作自然会开发出相关的干预方案。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社区传播研究成果（和注释数据集）。该项目的网站（http://nlab.engr.uconn.edu/sch.html）提供了关于研究和结果的更多信息。