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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Evaluating alignment quality between iconic language and reference terminologies using similarity metrics
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, March 2014
|
| DOI | 10.1186/1472-6947-14-17 |
| Pubmed ID | |
| Authors |
Nicolas Griffon, Gaetan Kerdelhué, Lina F Soualmia, Tayeb Merabti, Julien Grosjean, Jean-Baptiste Lamy, Alain Venot, Catherine Duclos, Stefan J Darmoni |
| Abstract |
Visualization of Concepts in Medicine (VCM) is a compositional iconic language that aims to ease information retrieval in Electronic Health Records (EHR), clinical guidelines or other medical documents. Using VCM language in medical applications requires alignment with medical reference terminologies. Alignment from Medical Subject Headings (MeSH) thesaurus and International Classification of Diseases - tenth revision (ICD10) to VCM are presented here. This study aim was to evaluate alignment quality between VCM and other terminologies using different measures of inter-alignment agreement before integration in EHR. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Practitioners (doctors, other healthcare professionals) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 32 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Australia | 1 | 3% |
| Unknown | 31 | 97% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 19% |
| Researcher | 6 | 19% |
| Student > Master | 5 | 16% |
| Librarian | 2 | 6% |
| Professor > Associate Professor | 2 | 6% |
| Other | 4 | 13% |
| Unknown | 7 | 22% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 34% |
| Computer Science | 6 | 19% |
| Nursing and Health Professions | 2 | 6% |
| Engineering | 2 | 6% |
| Social Sciences | 2 | 6% |
| Other | 2 | 6% |
| Unknown | 7 | 22% |
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 17 March 2014.
All research outputs
#18,367,612
of 22,749,166 outputs
Outputs from BMC Medical Informatics and Decision Making
#1,567
of 1,985 outputs
Outputs of similar age
#160,530
of 220,818 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#21
of 27 outputs
Altmetric has tracked 22,749,166 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 1,985 research outputs from this source. They receive a mean Attention Score of 4.9. 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 220,818 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 27 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.