Literature DB >> 33871868

Changing Health-Related Behaviors 6: Analysis, Interpretation, and Application of Big Data.

Randy Giffen1, Donald Bryant2.   

Abstract

The behavior of individuals can affect both their own health and the health of those around them. Furthermore, the behavior of healthcare providers obviously affects the health of those receiving the care. In both of these cases, and in spite of its known benefits, behavior change is difficult for most people. To make change easier, big data can provide insight through an objective and nonjudgmental perspective. It may also help make specific, individualized, evidence-based recommendations for effective change. We provide a historical perspective on data and health and then describe the value of adding big data systems and how they are implemented. We discuss some of the sources of big data and how it is collected. We also review the additional challenges for analysis, interpretation, and application of big data that require specific technologies. We end with a summary of current uses of big data for behavior change and suggestions for additional approaches, which may be of benefit.

Entities:  

Keywords:  Analytics; Behavior change; Big data; Machine learning

Year:  2021        PMID: 33871868     DOI: 10.1007/978-1-0716-1138-8_34

Source DB:  PubMed          Journal:  Methods Mol Biol        ISSN: 1064-3745


  17 in total

Review 1.  Artificial intelligence in healthcare.

Authors:  Kun-Hsing Yu; Andrew L Beam; Isaac S Kohane
Journal:  Nat Biomed Eng       Date:  2018-10-10       Impact factor: 25.671

2.  The Tension Between Big Data and Theory in the "Omics" Era of Biomedical Research.

Authors:  Sui Huang
Journal:  Perspect Biol Med       Date:  2018       Impact factor: 1.416

3.  Big Data: Will It Improve Patient-Centered Care?

Authors:  Denzil G Fiebig
Journal:  Patient       Date:  2017-04       Impact factor: 3.883

4.  Evidence for a limit to human lifespan.

Authors:  Xiao Dong; Brandon Milholland; Jan Vijg
Journal:  Nature       Date:  2016-10-05       Impact factor: 49.962

5.  AI-augmented multidisciplinary teams: hype or hope?

Authors:  Antonio Di Ieva
Journal:  Lancet       Date:  2019-11-05       Impact factor: 79.321

Review 6.  The answer is 17 years, what is the question: understanding time lags in translational research.

Authors:  Zoë Slote Morris; Steven Wooding; Jonathan Grant
Journal:  J R Soc Med       Date:  2011-12       Impact factor: 5.344

7.  International evaluation of an AI system for breast cancer screening.

Authors:  Scott Mayer McKinney; Marcin Sieniek; Varun Godbole; Jonathan Godwin; Natasha Antropova; Hutan Ashrafian; Trevor Back; Mary Chesus; Greg S Corrado; Ara Darzi; Mozziyar Etemadi; Florencia Garcia-Vicente; Fiona J Gilbert; Mark Halling-Brown; Demis Hassabis; Sunny Jansen; Alan Karthikesalingam; Christopher J Kelly; Dominic King; Joseph R Ledsam; David Melnick; Hormuz Mostofi; Lily Peng; Joshua Jay Reicher; Bernardino Romera-Paredes; Richard Sidebottom; Mustafa Suleyman; Daniel Tse; Kenneth C Young; Jeffrey De Fauw; Shravya Shetty
Journal:  Nature       Date:  2020-01-01       Impact factor: 49.962

8.  Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review.

Authors:  Cao Xiao; Edward Choi; Jimeng Sun
Journal:  J Am Med Inform Assoc       Date:  2018-10-01       Impact factor: 4.497

Review 9.  Big Data Management for Healthcare Systems: Architecture, Requirements, and Implementation.

Authors:  Naoual El Aboudi; Laila Benhlima
Journal:  Adv Bioinformatics       Date:  2018-06-21

Review 10.  5G and intelligence medicine-how the next generation of wireless technology will reconstruct healthcare?

Authors:  Dong Li
Journal:  Precis Clin Med       Date:  2019-10-18
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