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ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data

Overview of attention for article published in Computer Networks, February 2017
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  • Average Attention Score compared to outputs of the same age
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

patent
1 patent

Citations

dimensions_citation
65 Dimensions

Readers on

mendeley
150 Mendeley
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Title
ViSiBiD: A learning model for early discovery and real-time prediction of severe clinical events using vital signs as big data
Published in
Computer Networks, February 2017
DOI 10.1016/j.comnet.2016.12.019
Authors

Abdur Rahim Mohammad Forkan, Ibrahim Khalil, Mohammed Atiquzzaman

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 150 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
France 1 <1%
Canada 1 <1%
Unknown 148 99%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 32 21%
Student > Master 22 15%
Student > Bachelor 14 9%
Student > Doctoral Student 11 7%
Lecturer 8 5%
Other 24 16%
Unknown 39 26%
Readers by discipline Count As %
Computer Science 42 28%
Engineering 29 19%
Medicine and Dentistry 10 7%
Business, Management and Accounting 6 4%
Nursing and Health Professions 5 3%
Other 13 9%
Unknown 45 30%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 14 November 2019.
All research outputs
#8,534,528
of 25,373,627 outputs
Outputs from Computer Networks
#409
of 1,238 outputs
Outputs of similar age
#149,356
of 424,905 outputs
Outputs of similar age from Computer Networks
#5
of 14 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 43rd percentile – i.e., 43% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,238 research outputs from this source. They receive a mean Attention Score of 4.2. This one is in the 25th percentile – i.e., 25% 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 424,905 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 50% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one is in the 35th percentile – i.e., 35% of its contemporaries scored the same or lower than it.