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Exploiting mutual information for the imputation of static and dynamic mixed-type clinical data with an adaptive k-nearest neighbours approach

Overview of attention for article published in BMC Medical Informatics and Decision Making, August 2020
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Mentioned by

twitter
1 tweeter

Citations

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1 Dimensions

Readers on

mendeley
13 Mendeley
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Title
Exploiting mutual information for the imputation of static and dynamic mixed-type clinical data with an adaptive k-nearest neighbours approach
Published in
BMC Medical Informatics and Decision Making, August 2020
DOI 10.1186/s12911-020-01166-2
Authors

Erica Tavazzi, Sebastian Daberdaku, Rosario Vasta, Andrea Calvo, Adriano Chiò, Barbara Di Camillo

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

Geographical breakdown

Country Count As %
Unknown 13 100%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 5 38%
Professor > Associate Professor 2 15%
Student > Master 2 15%
Student > Doctoral Student 1 8%
Unknown 3 23%
Readers by discipline Count As %
Computer Science 4 31%
Medicine and Dentistry 2 15%
Social Sciences 1 8%
Pharmacology, Toxicology and Pharmaceutical Science 1 8%
Neuroscience 1 8%
Other 0 0%
Unknown 4 31%

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 20 August 2020.
All research outputs
#14,979,599
of 18,702,874 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,355
of 1,685 outputs
Outputs of similar age
#228,617
of 307,948 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#4
of 6 outputs
Altmetric has tracked 18,702,874 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,685 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 7th percentile – i.e., 7% 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 307,948 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 6 others from the same source and published within six weeks on either side of this one. This one has scored higher than 2 of them.