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Attention Score in Context
| Title |
Using Semantic Web technology to support icd-11 textual definitions authoring
|
|---|---|
| Published in |
Journal of Biomedical Semantics, April 2013
|
| DOI | 10.1186/2041-1480-4-11 |
| Pubmed ID | |
| Authors |
Guoqian Jiang, Harold R Solbrig, Christopher G Chute |
| Abstract |
The beta phase of the 11th revision of International Classification of Diseases (ICD-11) intends to accept public input through a distributed model of authoring. One of the core use cases is to create textual definitions for the ICD categories. The objective of the present study is to design, develop, and evaluate approaches to support ICD-11 textual definitions authoring using Semantic Web technology. We investigated a number of heterogeneous resources related to the definitions of diseases, including the linked open data (LOD) from DBpedia, the textual definitions from the Unified Medical Language System (UMLS) and the formal definitions of the Systematized Nomenclature of Medicine-Clinical Terms (SNOMED CT). We integrated them in a Semantic Web framework (i.e., the Linked Data in a Resource Description Framework [RDF] triple store), which is being proposed as a backend in a prototype platform for collaborative authoring of ICD-11 beta. We performed a preliminary evaluation on the usefulness of our approaches and discussed the potential challenges from both technical and clinical perspectives. |
X Demographics
The data shown below were collected from the profiles of 2 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 Kingdom | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 41 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Brazil | 2 | 5% |
| Netherlands | 1 | 2% |
| United Kingdom | 1 | 2% |
| Belgium | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 35 | 85% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 24% |
| Student > Master | 7 | 17% |
| Student > Ph. D. Student | 5 | 12% |
| Student > Bachelor | 3 | 7% |
| Professor | 3 | 7% |
| Other | 9 | 22% |
| Unknown | 4 | 10% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 17 | 41% |
| Medicine and Dentistry | 14 | 34% |
| Agricultural and Biological Sciences | 4 | 10% |
| Social Sciences | 1 | 2% |
| Engineering | 1 | 2% |
| Other | 0 | 0% |
| Unknown | 4 | 10% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 12 August 2015.
All research outputs
#16,046,765
of 25,373,627 outputs
Outputs from Journal of Biomedical Semantics
#218
of 368 outputs
Outputs of similar age
#123,278
of 209,481 outputs
Outputs of similar age from Journal of Biomedical Semantics
#6
of 7 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 368 research outputs from this source. They receive a mean Attention Score of 4.6. This one is in the 37th percentile – i.e., 37% of its peers scored the same or lower than it.
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We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one.