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Big Data in Public Health: Terminology, Machine Learning, and Privacy
Open accessAbstract
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.
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“Data-induced bias refers to the bias when the training data is not representative or insufficient that affects the algorithm model”
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Background
Metadata
- Authors
- Mooney, S. J., & Pejaver, V.
- Affiliations
- University of Washington (US)
- Venue
- Annual Review of Public Health
- Publisher
- Annual Reviews
- Published
- 2018-04-01
- Volume
- 39
- Issue
- 1
- Pages
- 95-112
- ISSN
- 0163-7525 (ISSN-L)
- 1545-2093
- PMC
- PMC6394411
- OpenAlex
- W2781122472
- Cited by
- 325
- References
- 139
- Citations by year
- 2026: 18 · 2025: 30 · 2024: 36 · 2023: 42 · 2022: 48 · 2021: 55 · 2020: 63 · 2019: 21 · 2018: 12
- FWCI
- 6.92
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- Annual Review of Public Health (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- Funders
- Washington Research Foundation
- National Institutes of Health (2t32hd057822-16)
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (T32, HD057822)
- Topics
- Social Sciences › Decision Sciences › Information Systems and Management › Big Data Technologies and Applications
- Health Sciences › Medicine › Epidemiology › Data-Driven Disease Surveillance
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Keywords
- big data, public health, machine learning, privacy, surveillance (medicine), hypothesis generation, causal inference, big data ethics, data science education, research groups
- MeSH terms
- Machine Learning, Big Data, Confidentiality, Humans, Terminology as Topic, Population Surveillance, Public Health, Research, Causality
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Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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