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.
Students that have been supported in full or part by this award include:
- Vishwali Mhasawade
- Harvineet Singh
- Hosting two high school students each summer for research projects through the NYU ARISE (Applied Research Innovations in Science and Engineering) program in 2019, 2020, 2021, 2022
- Designing a new Machine Learning in Public Health course at NYU School of Global Public Health
- Organized the first and recurring Machine Learning in Public Health workshop at NeurIPS (2020, 2021).
This material is based upon work supported by the National Science Foundation under Grant No. 1845487.
Disclaimer: Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation
Last update: Apr. 21, 2022
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