ReviewAnnual review of public health2018
Big Data in Public Health: Terminology, Machine Learning, and Privacy.
Review in Annual review of public health, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 117 papers, 4 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
117 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Ethical issues raised by artificial intelligence and big data in population health: a scoping review.Frontiers in sociology · 2025Pooled it
- Diagnostic performance of machine-learning algorithms for sepsis prediction: An updated meta-analysis.Technology and health care : official journal of the European Society for Engineering and Medicine · 2024Pooled it
- Use of Social Media Data to Diagnose and Monitor Psychotic Disorders: Systematic Review.Journal of medical Internet research · 2022Pooled it
- A systematic review of supermarket automated electronic sales data for population dietary surveillance.Nutrition reviews · 2022Pooled it
- An Exploration of Machine Learning Methods in Human Biomonitoring.International journal of environmental research and public health · 2026Review
- Impact of Pollution on Mental Health: A Systematic Review of Associations, Methodological Challenges, and Future Directions.Health science reports · 2026Review
- Machine learning models for predicting childhood anemia in Mozambique: analysis from national survey data.BMC pediatrics · 2026Article
- Article
- Performance of predictive AI-based clinical decision support systems across clinical domains: A systematic review and meta-analysis.PLOS digital health · 2026Article
- Article
- Building data science capacity in the public health workforce: a Public Health Data Science Life Cycle framework.Frontiers in public health · 2026Review
- AI powered patient records analysis for injury surveillance in children and adolescents - a feasibility study from Switzerland.International journal of public health · 2026Article
- Forward Outlook on Oral Health: The American Association of Public Health Dentistry's 2025 Research Agenda for Dental Public Health.Journal of public health dentistry · 2025Article
- Advancing equitable and sustainable urban health.Nature medicine · 2025Review
- Evaluating trustworthiness in AI-Based diabetic retinopathy screening: addressing transparency, consent, and privacy challenges.BMC medical ethics · 2025Article
- Prevalence and factors associated with HIV drug resistance among adult persons living with HIV/AIDS in nine countries of Sub-Saharan Africa using population-based HIV impact assessments: 2015-2019.BMC public health · 2025Article
- Comparison of Machine Learning Algorithms Identifying Children at Increased Risk of Out-of-Home Placement: Development and Practical Considerations.Health services research · 2025Article
- Consensus recommendations for measuring the impact of contraception on the menstrual cycle in contraceptive clinical trials.Contraception · 2025Article
- Leveraging Administrative Health Databases to Address Health Challenges in Farming Populations: Scoping Review and Bibliometric Analysis (1975-2024).JMIR public health and surveillance · 2025Article
- How to select predictive models for decision-making or causal inference.GigaScience · 2025Article
57 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.