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

New York University, New York NY

Investigators

Abstract

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.