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Open AccessJournal ArticleDOI

Making sense of big data in health research: Towards an EU action plan

Charles Auffray, +59 more
- 23 Jun 2016 - 
- Vol. 8, Iss: 1, pp 71-71
TLDR
Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively and contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.
Abstract
Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of “big data” for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.

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Journal ArticleDOI

Medical Internet of Things and Big Data in Healthcare

TL;DR: A new category of "personalised preventative health coaches" (Digital Health Advisors) will emerge that will possess the skills and the ability to interpret and understand health and well-being data and help their clients avoid chronic and diet-related illness, improve cognitive function, achieve improved mental health and achieve improved lifestyles overall.
Journal ArticleDOI

Automated machine learning: Review of the state-of-the-art and opportunities for healthcare.

TL;DR: The existing literature in the field of automated machine learning (AutoML) is reviewed to help healthcare professionals better utilize machine learning models "off-the-shelf" with limited data science expertise to help there to be widespread adoption of AutoML in healthcare.
Journal ArticleDOI

Concurrence of big data analytics and healthcare: A systematic review

TL;DR: There is a paucity of information on evidence of real-world use of Big Data analytics in healthcare, and majority of the studies were from developed countries which brings out the need for promotion of research on Healthcare Big Data Analytics in developing countries.
Journal ArticleDOI

Integrated Omics: Tools, Advances, and Future Approaches

TL;DR: This work discusses recent approaches, existing tools, and potential caveats in the integration of omics datasets for development of standardized analytical pipelines that could be adopted by the global omics research community.
Journal ArticleDOI

A Systematic Review on Healthcare Analytics: Application and Theoretical Perspective of Data Mining

TL;DR: It is found that the existing literature mostly examines analytics in clinical and administrative decision-making, and analytics based on website and social media data has been increasing in recent years.
References
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Journal ArticleDOI

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Journal Article

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Kyle Chang, +337 more
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Journal ArticleDOI

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Journal ArticleDOI

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