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Attention Score in Context
| Title |
Rewriting and suppressing UMLS terms for improved biomedical term identification
|
|---|---|
| Published in |
Journal of Biomedical Semantics, March 2010
|
| DOI | 10.1186/2041-1480-1-5 |
| Pubmed ID | |
| Authors |
Kristina M Hettne, Erik M van Mulligen, Martijn J Schuemie, Bob JA Schijvenaars, Jan A Kors |
| Abstract |
Identification of terms is essential for biomedical text mining.. We concentrate here on the use of vocabularies for term identification, specifically the Unified Medical Language System (UMLS). To make the UMLS more suitable for biomedical text mining we implemented and evaluated nine term rewrite and eight term suppression rules. The rules rely on UMLS properties that have been identified in previous work by others, together with an additional set of new properties discovered by our group during our work with the UMLS. Our work complements the earlier work in that we measure the impact on the number of terms identified by the different rules on a MEDLINE corpus. The number of uniquely identified terms and their frequency in MEDLINE were computed before and after applying the rules. The 50 most frequently found terms together with a sample of 100 randomly selected terms were evaluated for every rule. |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 4 | 7% |
| Portugal | 3 | 5% |
| United Kingdom | 2 | 4% |
| Spain | 2 | 4% |
| Belgium | 1 | 2% |
| Australia | 1 | 2% |
| China | 1 | 2% |
| United States | 1 | 2% |
| Unknown | 41 | 73% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 18 | 32% |
| Student > Ph. D. Student | 16 | 29% |
| Other | 5 | 9% |
| Professor > Associate Professor | 4 | 7% |
| Student > Master | 4 | 7% |
| Other | 6 | 11% |
| Unknown | 3 | 5% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 28 | 50% |
| Agricultural and Biological Sciences | 15 | 27% |
| Linguistics | 2 | 4% |
| Business, Management and Accounting | 1 | 2% |
| Unspecified | 1 | 2% |
| Other | 5 | 9% |
| Unknown | 4 | 7% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 January 2015.
All research outputs
#2,313,338
of 25,373,627 outputs
Outputs from Journal of Biomedical Semantics
#26
of 368 outputs
Outputs of similar age
#8,307
of 103,457 outputs
Outputs of similar age from Journal of Biomedical Semantics
#1
of 3 outputs
Altmetric has tracked 25,373,627 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 368 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 92% of its peers.
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 103,457 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 91% of its contemporaries.
We're also able to compare this research output to 3 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them