CAREER: Realizing the Potential of Behavioral Data Science for Population Health

职业：实现行为数据科学对人口健康的潜力

• 批准号: 2142794
• 负责人: Tim Althoff
• 金额: $ 59.92万
• 依托单位: University of Washington
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-10-01 至 2027-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2142794&HistoricalAwards=false
• 关键词: CAREER; Realizing; Potential; Behavioral; Data

CAREER: Realizing the Potential of Behavioral Data Science for Population HealthDetailed behavioral data from phones, watches, fitness trackers and health apps offer an unparalleled opportunity to quantify and act upon previously unmeasurable behavioral changes in order to improve mental health and accelerate responses to emerging diseases. This is possible because these conditions often manifest themselves through behavioral and physiological changes (e.g., reduced activity, increased heart rate, depressed mood). Currently, such conditions exact a massive toll, with mental health conditions representing 19% of all years of life lost to disability and premature mortality and viral infections, such as COVID-19 and influenza, rising to the third leading cause of death in the US in 2020. Despite the significant potential of increasingly available data, broad and tangible impacts have yet to be realized, in part due to the unique challenges of integrating and modeling a broad range of behavioral and health data. This project seeks to address these challenges by developing and sharing computational tools that will enable researchers, clinicians and practitioners to improve mental health treatment and more rapidly respond to emerging diseases. The project will also provide an integrated research and educational program by: (1) increasing high-school students' exposure to computer science with a focus on health and well-being applications; (2) creating and disseminating materials for high-school teachers to use in their classrooms; (3) preparing undergraduate students to address health and well-being challenges through an interdisciplinary data science class; and (4) broadly disseminating the results of this work through public open-source software and workshops for researchers and practitioners. The goal of this project is to develop a unified representation learning framework that addresses the unique challenges of modeling fine-grained behavioral data. Specifically, the learned compressed representations must: (1) be highly predictive in spite of the challenges of integrating heterogeneous data sources from sensors, devices, app use, demographic and health information (e.g., highly seasonal time series, discrete events, and static features), (2) effectively generalize to new users, populations, and outcomes outside the training data and source domain, (3) be robust to commonly missing data, and (4) protect private identifying information when considering how to share data or models with others. Additionally, due to recruiting and participation costs, most behavioral health applications represent small data problems, making it particularly challenging to learn effective predictive models from individual datasets alone. To address these challenges, this project will develop and integrate new methods for representation learning, self-supervision, transfer learning, robustness to missing data, and the protection of identifying information. The research team will demonstrate and evaluate the performance of the representation learning framework across a diverse set of health applications, including behavioral monitoring of influenza and COVID-19 symptoms and personalizing sleep and mental health interventions. With these advancements, the project seeks to enable rapid model customization, significantly reduce the expertise and effort required to build new behavioral health research and applications, and help scientists and health professionals answer fundamental research questions about the impact of behavioral health conditions and the design of personalized interventions.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

职业：来自手机、手表、健身追踪器和健康应用程序的详细行为数据提供了一个无与伦比的机会，可以量化和应对以前无法衡量的行为变化，以改善心理健康并加速对新兴疾病的反应。这是可能的，因为这些条件通常通过行为和生理变化表现出来（例如，活动减少、心率加快、情绪低落）。目前，此类疾病造成了巨大的损失，精神健康状况占因残疾和过早死亡以及病毒感染（如COVID-19和流感）而损失的所有生命年数的19%，于二零二零年上升为美国的第三大死亡原因。尽管越来越多的可用数据具有巨大的潜力，但尚未实现广泛和切实的影响，部分原因是整合和建模广泛的行为和健康数据的独特挑战。该项目旨在通过开发和共享计算工具来应对这些挑战，这些工具将使研究人员，临床医生和从业人员能够改善心理健康治疗并更快地应对新出现的疾病。该项目还将通过以下方式提供综合研究和教育计划：（1）增加高中学生接触计算机科学，重点是健康和福祉应用;（2）创建和传播高中教师在课堂上使用的材料;（3）通过跨学科数据科学课程帮助本科生应对健康和福祉挑战;以及（4）通过面向研究人员和从业人员的公共开源软件和研讨会广泛传播这项工作的成果。该项目的目标是开发一个统一的表示学习框架，以解决建模细粒度行为数据的独特挑战。具体地，学习的压缩表示必须：（1）尽管存在集成来自传感器、设备、应用程序使用、人口统计和健康信息（例如，高度季节性的时间序列、离散事件和静态特征），（2）有效地推广到训练数据和源域之外的新用户、人群和结果，（3）对通常缺失的数据具有鲁棒性，以及（4）在考虑如何与他人共享数据或模型时保护私人识别信息。此外，由于招募和参与成本，大多数行为健康应用程序都存在小数据问题，因此仅从单个数据集学习有效的预测模型特别具有挑战性。为了应对这些挑战，该项目将开发和整合表征学习、自我监督、迁移学习、对缺失数据的鲁棒性以及识别信息保护的新方法。研究团队将展示和评估表征学习框架在各种健康应用中的性能，包括流感和COVID-19症状的行为监测以及个性化睡眠和心理健康干预。有了这些进步，该项目旨在实现快速模型定制，大大减少建立新的行为健康研究和应用所需的专业知识和工作，并帮助科学家和卫生专业人员回答有关行为健康状况的影响和个性化干预设计的基础研究问题。该奖项反映了NSF的法定使命，并被认为值得通过使用评估来支持。基金会的学术价值和更广泛的影响审查标准。

项目成果

Leveraging Mobile Technology for Public Health Promotion: A Multidisciplinary Perspective.

• DOI: 10.1146/annurev-publhealth-060220-041643
• 发表时间: 2023-04-03
• 期刊: ANNUAL REVIEW OF PUBLIC HEALTH
• 影响因子: 20.8
• 作者: Hicks, Jennifer L.;Boswell, Melissa A.;Althoff, Tim;Crum, Alia J.;Ku, Joy P.;Landay, James A.;Moya, Paula M. L.;Murnane, Elizabeth L.;Snyder, Michael P.;King, Abby C.;Delp, Scott L.
• 通讯作者: Delp, Scott L.

Homekit2020: A Benchmark for Time Series Classification on a Large Mobile Sensing Dataset with Laboratory Tested Ground Truth of Influenza Infections

Homekit2020：大型移动传感数据集的时间序列分类基准，具有实验室测试的流感感染的基本事实

• 发表时间: 2023
• 期刊: and Learning (CHIL
• 作者: Merrill, Mike A;Safranchik, Esteban;Kolbeinsson, Arinbjörn;Gade, Piyusha;Ramirez, Ernesto;Schmidt, Ludwig;Foschini, Luca;Althoff, Tim
• 通讯作者: Althoff, Tim

Self-supervised Pretraining and Transfer Learning Enable Flu and COVID-19 Predictions in Small Mobile Sensing Datasets

• DOI: 10.48550/arxiv.2205.13607
• 发表时间: 2022-05
• 作者: Michael Merrill;Tim Althoff

GLOBEM: Multi-Year Datasets for Longitudinal Human Behavior Modeling Generalization

GLOBEM：用于纵向人类行为建模泛化的多年数据集

• 发表时间: 2023
• 期刊: NeurIPS: 36th Conference on Neural Information Processing Systems
• 作者: Xuhai Xu, Han Zhang

Gendered Mental Health Stigma in Masked Language Models

蒙面语言模型中的性别心理健康耻辱

• 发表时间: 2022
• 期刊: Conference on Empirical Methods in Natural Language Processing
• 作者: Inna Wanyin Lin;Lucille Njoo;Anjalie Field;Ashish Sharma;Katharina Reinecke;Tim Althoff;Yulia Tsvetkov
• 通讯作者: Yulia Tsvetkov