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Natural language processing of radiology reports for identification of skeletal site-specific fractures

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2019
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Mentioned by

twitter
1 tweeter

Citations

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1 Dimensions

Readers on

mendeley
9 Mendeley
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Title
Natural language processing of radiology reports for identification of skeletal site-specific fractures
Published in
BMC Medical Informatics and Decision Making, April 2019
DOI 10.1186/s12911-019-0780-5
Pubmed ID
Authors

Yanshan Wang, Saeed Mehrabi, Sunghwan Sohn, Elizabeth J. Atkinson, Shreyasee Amin, Hongfang Liu

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 9 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 9 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 2 22%
Student > Master 2 22%
Professor 1 11%
Other 1 11%
Student > Postgraduate 1 11%
Other 2 22%
Readers by discipline Count As %
Medicine and Dentistry 4 44%
Computer Science 2 22%
Social Sciences 2 22%
Unspecified 1 11%

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 05 April 2019.
All research outputs
#12,048,164
of 13,589,098 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,166
of 1,224 outputs
Outputs of similar age
#217,432
of 255,698 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#6
of 13 outputs
Altmetric has tracked 13,589,098 research outputs across all sources so far. This one is in the 1st percentile – i.e., 1% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,224 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.1. This one is in the 1st percentile – i.e., 1% 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 255,698 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 13 others from the same source and published within six weeks on either side of this one. This one is in the 1st percentile – i.e., 1% of its contemporaries scored the same or lower than it.