↓ Skip to main content

Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data

Overview of attention for article published in Giga Science, February 2016
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)

Mentioned by

twitter
13 tweeters
peer_reviews
1 peer review site
facebook
1 Facebook page

Citations

dimensions_citation
59 Dimensions

Readers on

mendeley
223 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Methodological challenges and analytic opportunities for modeling and interpreting Big Healthcare Data
Published in
Giga Science, February 2016
DOI 10.1186/s13742-016-0117-6
Pubmed ID
Authors

Ivo D. Dinov

Abstract

Managing, processing and understanding big healthcare data is challenging, costly and demanding. Without a robust fundamental theory for representation, analysis and inference, a roadmap for uniform handling and analyzing of such complex data remains elusive. In this article, we outline various big data challenges, opportunities, modeling methods and software techniques for blending complex healthcare data, advanced analytic tools, and distributed scientific computing. Using imaging, genetic and healthcare data we provide examples of processing heterogeneous datasets using distributed cloud services, automated and semi-automated classification techniques, and open-science protocols. Despite substantial advances, new innovative technologies need to be developed that enhance, scale and optimize the management and processing of large, complex and heterogeneous data. Stakeholder investments in data acquisition, research and development, computational infrastructure and education will be critical to realize the huge potential of big data, to reap the expected information benefits and to build lasting knowledge assets. Multi-faceted proprietary, open-source, and community developments will be essential to enable broad, reliable, sustainable and efficient data-driven discovery and analytics. Big data will affect every sector of the economy and their hallmark will be 'team science'.

Twitter Demographics

The data shown below were collected from the profiles of 13 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 223 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 <1%
Brazil 2 <1%
Ecuador 1 <1%
Canada 1 <1%
Switzerland 1 <1%
Unknown 216 97%

Demographic breakdown

Readers by professional status Count As %
Student > Master 46 21%
Student > Ph. D. Student 38 17%
Researcher 36 16%
Student > Doctoral Student 15 7%
Professor > Associate Professor 14 6%
Other 45 20%
Unknown 29 13%
Readers by discipline Count As %
Computer Science 74 33%
Medicine and Dentistry 27 12%
Engineering 19 9%
Biochemistry, Genetics and Molecular Biology 12 5%
Agricultural and Biological Sciences 12 5%
Other 44 20%
Unknown 35 16%

Attention Score in Context

This research output has an Altmetric Attention Score of 9. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 25 October 2018.
All research outputs
#1,798,257
of 13,200,341 outputs
Outputs from Giga Science
#367
of 580 outputs
Outputs of similar age
#46,038
of 267,043 outputs
Outputs of similar age from Giga Science
#1
of 1 outputs
Altmetric has tracked 13,200,341 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 580 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 23.2. This one is in the 36th percentile – i.e., 36% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 267,043 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 82% of its contemporaries.
We're also able to compare this research output to 1 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them