ReviewYearbook of medical informatics2014
Big Data in Science and Healthcare: A Review of Recent Literature and Perspectives. Contribution of the IMIA Social Media Working Group.
Review in Yearbook of medical informatics, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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
30 citing papers in PubMed.
- Exploring data reduction strategies in the analysis of continuous pressure imaging technology.BMC medical research methodology · 2023Trial
- The impact of internet use on physical activity among Chinese older women: the mediating role of social support and intrinsic motivation.Frontiers in public health · 2026Article
- Effectiveness of AI-generated orthodontic treatment plans compared to expert orthodontist recommendations: a cross-sectional pilot study.Dental press journal of orthodontics · 2025Observational
- Exploring the Evolution of Social Media in Mental Health Interventions: A Mapping Review.Yearbook of medical informatics · 2023Review
- The impact of commercial health datasets on medical research and health-care algorithms.The Lancet. Digital health · 2023Review
- Perspective Toward Machine Learning Implementation in Pediatric Medicine: Mixed Methods Study.JMIR medical informatics · 2022Article
- Towards the Use of Big Data in Healthcare: A Literature Review.Healthcare (Basel, Switzerland) · 2022Review
- From Syndemic Lesson after COVID-19 Pandemic to a "Systemic Clinical Risk Management" Proposal in the Perspective of the Ethics of Job Well Done.International journal of environmental research and public health · 2021Article
- Development and utility assessment of a machine learning bloodstream infection classifier in pediatric patients receiving cancer treatments.BMC cancer · 2020Article
- Artificial Intelligence and Big Data in Diabetes Care: A Position Statement of the Italian Association of Medical Diabetologists.Journal of medical Internet research · 2020Article
- Personalised medicine in knee arthroplasty: we need more science!Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2019Article
- Guidelines and recommendations for ensuring Good Epidemiological Practice (GEP): a guideline developed by the German Society for Epidemiology.European journal of epidemiology · 2019Article
- Article
- Risk Assessment Tools and Data-Driven Approaches for Predicting and Preventing Suicidal Behavior.Frontiers in psychiatry · 2019Article
- Big Data Research in Chronic Kidney Disease.Chinese medical journal · 2018Article
- National Healthcare Service and Its Big Data Analytics.Healthcare informatics research · 2018Article
- Development, Feasibility, and Small-Scale Implementation of a Web-Based Prognostic Tool-Surveillance, Epidemiology, and End Results Cancer Survival Calculator.JMIR cancer · 2017Article
- Menopause and big data: Word Adjacency Graph modeling of menopause-related ChaCha data.Menopause (New York, N.Y.) · 2017Article
- Quantifying the Impact of Trainee Providers on Outpatient Clinic Workflow using Secondary EHR Data.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Using Distributed Data over HBase in Big Data Analytics Platform for Clinical Services.Computational and mathematical methods in medicine · 2017Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesAs technology continues to evolve and rise in various industries, such as healthcare, science, education, and gaming, a sophisticated concept known as Big Data is surfacing. The concept of analytics aims to understand data. We set out to portray and discuss perspectives of the evolving use of Big Data in science and healthcare and, to examine some of the opportunities and challenges.
methodsA literature review was conducted to highlight the implications associated with the use of Big Data in scientific research and healthcare innovations, both on a large and small scale.
resultsScientists and health-care providers may learn from one another when it comes to understanding the value of Big Data and analytics. Small data, derived by patients and consumers, also requires analytics to become actionable. Connectivism provides a framework for the use of Big Data and analytics in the areas of science and healthcare. This theory assists individuals to recognize and synthesize how human connections are driving the increase in data. Despite the volume and velocity of Big Data, it is truly about technology connecting humans and assisting them to construct knowledge in new ways. Concluding Thoughts: The concept of Big Data and associated analytics are to be taken seriously when approaching the use of vast volumes of both structured and unstructured data in science and health-care. Future exploration of issues surrounding data privacy, confidentiality, and education are needed. A greater focus on data from social media, the quantified self-movement, and the application of analytics to "small data" would also be useful.
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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.