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Acute Kidney Injury in the Era of Big Data: The 15th Consensus Conference of the Acute Dialysis Quality Initiative (ADQI)

Overview of attention for article published in Canadian Journal of Kidney Health and Disease, February 2016
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Mentioned by

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1 tweeter
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1 Facebook page

Citations

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31 Dimensions

Readers on

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73 Mendeley
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Title
Acute Kidney Injury in the Era of Big Data: The 15th Consensus Conference of the Acute Dialysis Quality Initiative (ADQI)
Published in
Canadian Journal of Kidney Health and Disease, February 2016
DOI 10.1186/s40697-016-0103-z
Pubmed ID
Authors

Sean M. Bagshaw, Stuart L. Goldstein, Claudio Ronco, John A. Kellum

Abstract

The world is immersed in "big data". Big data has brought about radical innovations in the methods used to capture, transfer, store and analyze the vast quantities of data generated every minute of every day. At the same time; however, it has also become far easier and relatively inexpensive to do so. Rapidly transforming, integrating and applying this large volume and variety of data are what underlie the future of big data. The application of big data and predictive analytics in healthcare holds great promise to drive innovation, reduce cost and improve patient outcomes, health services operations and value. Acute kidney injury (AKI) may be an ideal syndrome from which various dimensions and applications built within the context of big data may influence the structure of services delivery, care processes and outcomes for patients. The use of innovative forms of "information technology" was originally identified by the Acute Dialysis Quality Initiative (ADQI) in 2002 as a core concept in need of attention to improve the care and outcomes for patients with AKI. For this 15(th) ADQI consensus meeting held on September 6-8, 2015 in Banff, Canada, five topics focused on AKI and acute renal replacement therapy were developed where extensive applications for use of big data were recognized and/or foreseen. In this series of articles in the Canadian Journal of Kidney Health and Disease, we describe the output from these discussions.

Twitter Demographics

The data shown below were collected from the profile of 1 tweeter 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 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 1 1%
Unknown 72 99%

Demographic breakdown

Readers by professional status Count As %
Student > Master 20 27%
Researcher 13 18%
Professor > Associate Professor 7 10%
Student > Ph. D. Student 7 10%
Student > Bachelor 5 7%
Other 13 18%
Unknown 8 11%
Readers by discipline Count As %
Medicine and Dentistry 27 37%
Nursing and Health Professions 10 14%
Business, Management and Accounting 5 7%
Decision Sciences 4 5%
Computer Science 4 5%
Other 12 16%
Unknown 11 15%

Attention Score in Context

This research output has an Altmetric Attention Score of 1. 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 22 March 2017.
All research outputs
#12,713,403
of 16,657,433 outputs
Outputs from Canadian Journal of Kidney Health and Disease
#309
of 355 outputs
Outputs of similar age
#174,165
of 268,886 outputs
Outputs of similar age from Canadian Journal of Kidney Health and Disease
#10
of 10 outputs
Altmetric has tracked 16,657,433 research outputs across all sources so far. This one is in the 20th percentile – i.e., 20% of other outputs scored the same or lower than it.
So far Altmetric has tracked 355 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one is in the 9th percentile – i.e., 9% 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 268,886 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 30th percentile – i.e., 30% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one.