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X Demographics
Mendeley readers
Attention Score in Context
Title |
A context-blocks model for identifying clinical relationships in patient records
|
---|---|
Published in |
BMC Bioinformatics, June 2011
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DOI | 10.1186/1471-2105-12-s3-s3 |
Pubmed ID | |
Authors |
Rezarta Islamaj Doğan, Aurélie Névéol, Zhiyong Lu |
Abstract |
Patient records contain valuable information regarding explanation of diagnosis, progression of disease, prescription and/or effectiveness of treatment, and more. Automatic recognition of clinically important concepts and the identification of relationships between those concepts in patient records are preliminary steps for many important applications in medical informatics, ranging from quality of care to hypothesis generation. |
X Demographics
The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 3 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 2 | 67% |
Scientists | 1 | 33% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
United States | 2 | 7% |
Netherlands | 1 | 4% |
Portugal | 1 | 4% |
Unknown | 23 | 85% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 12 | 44% |
Researcher | 7 | 26% |
Student > Master | 3 | 11% |
Student > Doctoral Student | 2 | 7% |
Professor > Associate Professor | 1 | 4% |
Other | 0 | 0% |
Unknown | 2 | 7% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 10 | 37% |
Medicine and Dentistry | 8 | 30% |
Agricultural and Biological Sciences | 4 | 15% |
Psychology | 2 | 7% |
Engineering | 1 | 4% |
Other | 0 | 0% |
Unknown | 2 | 7% |
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 15 June 2012.
All research outputs
#13,008,230
of 22,653,392 outputs
Outputs from BMC Bioinformatics
#3,964
of 7,236 outputs
Outputs of similar age
#76,241
of 112,444 outputs
Outputs of similar age from BMC Bioinformatics
#57
of 93 outputs
Altmetric has tracked 22,653,392 research outputs across all sources so far. This one is in the 42nd percentile – i.e., 42% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,236 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one is in the 44th percentile – i.e., 44% 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 112,444 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 31st percentile – i.e., 31% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 93 others from the same source and published within six weeks on either side of this one. This one is in the 38th percentile – i.e., 38% of its contemporaries scored the same or lower than it.