Big Data in Public Health: Terminology, Machine Learning, and Privacy

Posted: 18 Jun 2018

See all articles by Stephen J. Mooney

Stephen J. Mooney

University of Washington

Vikas Pejaver

University of Washington

Date Written: April 2018

Abstract

The digital world is generating data at a staggering and still increasing rate. While these “big data” have unlocked novel opportunities to understand public health, they hold still greater potential for research and practice. This review explores several key issues that have arisen around big data. First, we propose a taxonomy of sources of big data to clarify terminology and identify threads common across some subtypes of big data. Next, we consider common public health research and practice uses for big data, including surveillance, hypothesis-generating research, and causal inference, while exploring the role that machine learning may play in each use. We then consider the ethical implications of the big data revolution with particular emphasis on maintaining appropriate care for privacy in a world in which technology is rapidly changing social norms regarding the need for (and even the meaning of) privacy. Finally, we make suggestions regarding structuring teams and training to succeed in working with big data in research and practice.

Suggested Citation

Mooney, Stephen J. and Pejaver, Vikas, Big Data in Public Health: Terminology, Machine Learning, and Privacy (April 2018). Annual Review of Public Health, Vol. 39, pp. 95-112, 2018. Available at SSRN: https://ssrn.com/abstract=3197561 or http://dx.doi.org/10.1146/annurev-publhealth-040617-014208

Stephen J. Mooney (Contact Author)

University of Washington ( email )

Seattle, WA 98195
United States

Vikas Pejaver

University of Washington ( email )

Seattle, WA 98195
United States

Register to save articles to
your library

Register

Paper statistics

Abstract Views
87
PlumX Metrics
!

Under construction: SSRN citations will be offline until July when we will launch a brand new and improved citations service, check here for more details.

For more information