CAREER: Learning from When, Where and by Whom Data is Generated for Advancing Public Health Studies

职业：向何时、何地以及由谁生成数据学习以推进公共卫生研究

• 批准号: 1845487
• 负责人: Rumi Chunara
• 金额: $ 55万
• 依托单位: New York University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-06-01 至 2025-05-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1845487&HistoricalAwards=false
• 关键词: CAREER; Learning; When; Where; Whom

Improving disease prevention through robust and high-granularity measures of lifestyle, environmental and social factors from daily life will improve healthcare by enabling precise and focused proactive interventions. This will dramatically change the healthcare paradigm in this country and significantly reduce costs and illnesses, more so than a solely reactive focus on disease diagnosis and treatment. Public health is the study of these daily life factors and prevention efforts. New person-generated data (PGD) from Internet and mobile data sources, such as mHealth, social media, wearables, and data from smartphone apps, offer unprecedented opportunity to provide sub-daily, as well as local, neighborhood-level measures of lifestyle, environmental and social factors from daily life. However, the impact of this data has yet to be fully realized for public health efforts. In part, this is because existing research efforts on PGD often focus on processing the content of data in isolation, and do not consider human data sharing patterns, that is, who contributes the data, when it is contributed and from where it is contributed. By accounting for these attributes, this project aims to improve the validity and reliability of measures extracted from PGD and enable improved understanding of high-granularity health risks and outcomes. The project will also provide a highly-integrated research and educational program for public health practitioners, students, and community members in the context of PGD and public health by: (1) preparing students to use computer science in today's job landscape via a problem-based learning class; (2) increasing high-school students' exposure to computer science in the real-world with a focus on applications of computer science; and (3) disseminating scientific understanding of computer science in the public health and general community. In conjunction, this work will improve both computer science and public health practice and research through method development and exposure of diverse community members and community-oriented professionals to the utility of data mining and machine learning. The goal of this project is to develop new machine learning approaches motivated by the need to improve data management and analysis in the public health domain. The research addresses critical statistical and computational challenges due to human data sharing patterns. These challenges represent an opportunity for contributions to health informatics and machine learning by improving prediction efforts through learning from person-generated data in combination with "when, where and by whom" the data is generated. Using this information as an additional signal, this project explores: (1) inference of temporal patterns (motifs) by accounting for characteristic human data sharing patterns; (2) discovery of underlying latent spatial representation of content from humans that is noisy, sparse and inconsistently generated over space by using content jointly with geographic information; and (3) prediction in data without labels using data for the same task but from a different domain by including attributes of the population generating the data in each dataset.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.

通过实现精确和专注的积极主动的干预措施来改善生活方式，环境和社会因素的稳健和高粒度度量，改善疾病预防。这将极大地改变该国的医疗保健范式，并显着降低成本和疾病，而不是仅仅反应性地关注疾病诊断和治疗。公共卫生是对这些日常生活因素和预防工作的研究。来自Internet和移动数据源的新人数据（PGD），例如MHealth，社交媒体，可穿戴设备以及智能手机应用程序的数据，提供了前所未有的机会，可提供日常生活方式，环境和社会因素的日常工作，以及日常生活中的本地，邻里级别的度量。但是，这些数据的影响尚未完全实现，以实现公共卫生的工作。在某种程度上，这是因为对PGD的现有研究工作通常专注于孤立地处理数据的内容，并且不考虑人类数据共享模式，即贡献数据，何时贡献数据以及从贡献数据的地方进行贡献。通过考虑这些属性，该项目旨在提高从PGD提取的措施的有效性和可靠性，并能够提高对高颗粒性健康风险和结果的了解。该项目还将在PGD和公共卫生的背景下为公共卫生从业人员，学生和社区成员提供一项高度综合的研究和教育计划，作者：（1）通过基于问题的学习课程在当今的工作景观中使用计算机科学； （2）在现实世界中增加高中生对计算机科学的接触，重点是计算机科学的应用； （3）在公共卫生和一般社区中传播对计算机科学的科学理解。结合起来，这项工作将通过方法开发和暴露于各种社区成员以及面向社区的专业人员的方法来改善计算机科学和公共卫生实践以及研究，从而提高数据挖掘和机器学习的实用性。该项目的目的是开发由需要改善公共卫生领域数据管理和分析的新机器学习方法。该研究解决了由于人类数据共享模式而引起的关键统计和计算挑战。这些挑战代表了通过从人体生成的数据中学习，以及“何时，何处和由谁”生成数据，从而改善预测工作，从而为健康信息学和机器学习做出了贡献。将此信息用作附加信号，该项目探讨：（1）通过考虑特征性人类数据共享模式来推断时间模式（图案）； （2）通过与地理信息共同使用内容，发现了人类的潜在潜在空间表示，这些内容是嘈杂，稀疏且不一致地在空间上产生的； （3）在没有标签的数据中预测相同任务的数据，但通过包括在每个数据集中生成数据的人群的属性来从不同的领域进行预测。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响标准通过评估来评估的。

项目成果

Machine learning and algorithmic fairness in public and population health

• DOI: 10.1038/s42256-021-00373-4
• 发表时间: 2021-07-29
• 期刊: NATURE MACHINE INTELLIGENCE
• 影响因子: 23.8
• 作者: Mhasawade, Vishwali;Zhao, Yuan;Chunara, Rumi
• 通讯作者: Chunara, Rumi

Fairness Violations and Mitigation under Covariate Shift

• DOI: 10.1145/3442188.3445865
• 发表时间: 2019-11
• 期刊: Proceedings of the 2021 ACM Conference on Fairness, Accountability, and Transparency
• 作者: Harvineet Singh;Rina Singh;Vishwali Mhasawade;R. Chunara

Fair Predictors under Distribution Shift

• 发表时间: 2019-11
• 期刊: ArXiv
• 作者: Harvineet Singh;Rina Singh;Vishwali Mhasawade;R. Chunara

Causal Multi-level Fairness

因果多层次公平性

• DOI: 10.1145/3461702.3462587
• 发表时间: 2021
• 期刊: and Society
• 作者: Mhasawade, Vishwali;Chunara, Rumi
• 通讯作者: Chunara, Rumi

Role of the Built and Online Social Environments on Expression of Dining on Instagram

• DOI: 10.3390/ijerph17030735
• 发表时间: 2020-01
• 期刊: International Journal of Environmental Research and Public Health
• 作者: Vishwali Mhasawade;Anas Elghafari;D. Duncan;R. Chunara