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.

通过对日常生活中的生活方式、环境和社会因素进行强有力的高粒度测量来改善疾病预防，将通过实现精确和有针对性的积极干预来改善医疗保健。这将极大地改变这个国家的医疗保健模式，大大减少成本和疾病，而不仅仅是对疾病诊断和治疗的反应性关注。公共卫生是研究这些日常生活因素和预防工作的。来自互联网和移动的数据源（如移动健康、社交媒体、可穿戴设备和智能手机应用程序的数据）的新的个人生成数据（PGD）提供了前所未有的机会，可以提供日常生活中的生活方式、环境和社会因素的次日常以及本地、社区级别的测量。然而，这一数据对公共卫生工作的影响尚未充分实现。部分原因是，现有的PGD研究工作往往侧重于孤立地处理数据内容，而不考虑人类数据共享模式，即谁贡献了数据，何时贡献以及从何处贡献。通过考虑这些属性，该项目旨在提高从PGD中提取的措施的有效性和可靠性，并使人们能够更好地理解高粒度的健康风险和结果。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高度整合的研究和教育计划：（1）通过基于问题的学习课程，为学生在当今的工作环境中使用计算机科学做好准备;（2）增加高中生在现实世界中接触计算机科学的机会，重点是计算机科学的应用;及（3）在公共卫生及一般社区传播对计算机科学的科学认识。同时，这项工作将通过方法开发和不同社区成员和面向社区的专业人员接触数据挖掘和机器学习的实用性来改善计算机科学和公共卫生实践和研究。该项目的目标是开发新的机器学习方法，以满足改善公共卫生领域数据管理和分析的需求。该研究解决了人类数据共享模式带来的关键统计和计算挑战。这些挑战代表了通过从个人生成的数据中学习并结合“何时、何地和由谁”生成数据来改进预测工作，从而为健康信息学和机器学习做出贡献的机会。利用这些信息作为额外的信号，该项目探索：（1）通过考虑人类数据共享模式的特征来推断时间模式（图案）;（2）通过将内容与地理信息结合使用，发现人类在空间上产生的嘈杂、稀疏和不一致的内容的潜在空间表示;以及（3）通过在每个数据集中包括生成数据的总体属性，使用相同任务但来自不同领域的数据进行无标签的数据预测。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值进行评估，被认为值得支持和更广泛的影响审查标准。

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

Data Science in Public Health: Building Next Generation Capacity

公共卫生中的数据科学：建设下一代能力

• DOI: 10.1162/99608f92.18da72db
• 发表时间: 2022
• 期刊: Harvard Data Science Review
• 作者: Mirin, Nicholas;Mattie, Heather;Jackson, Latifa;Samad, Zainab;Chunara, Rumi
• 通讯作者: Chunara, Rumi