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ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials

Overview of attention for article published in BMC Medical Informatics and Decision Making, April 2012
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About this Attention Score

  • Good Attention Score compared to outputs of the same age (75th percentile)
  • High Attention Score compared to outputs of the same age and source (80th percentile)

Mentioned by

twitter
2 tweeters
wikipedia
1 Wikipedia page

Citations

dimensions_citation
17 Dimensions

Readers on

mendeley
58 Mendeley
citeulike
1 CiteULike
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Title
ASCOT: a text mining-based web-service for efficient search and assisted creation of clinical trials
Published in
BMC Medical Informatics and Decision Making, April 2012
DOI 10.1186/1472-6947-12-s1-s3
Pubmed ID
Authors

Ioannis Korkontzelos, Tingting Mu, Sophia Ananiadou

Abstract

Clinical trials are mandatory protocols describing medical research on humans and among the most valuable sources of medical practice evidence. Searching for trials relevant to some query is laborious due to the immense number of existing protocols. Apart from search, writing new trials includes composing detailed eligibility criteria, which might be time-consuming, especially for new researchers. In this paper we present ASCOT, an efficient search application customised for clinical trials. ASCOT uses text mining and data mining methods to enrich clinical trials with metadata, that in turn serve as effective tools to narrow down search. In addition, ASCOT integrates a component for recommending eligibility criteria based on a set of selected protocols.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters 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 58 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Portugal 1 2%
United Kingdom 1 2%
United Arab Emirates 1 2%
Taiwan 1 2%
Canada 1 2%
Unknown 51 88%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 11 19%
Researcher 10 17%
Student > Master 8 14%
Other 4 7%
Student > Doctoral Student 4 7%
Other 15 26%
Unknown 6 10%
Readers by discipline Count As %
Computer Science 17 29%
Medicine and Dentistry 13 22%
Agricultural and Biological Sciences 5 9%
Unspecified 3 5%
Decision Sciences 2 3%
Other 10 17%
Unknown 8 14%

Attention Score in Context

This research output has an Altmetric Attention Score of 4. 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 24 June 2015.
All research outputs
#3,134,320
of 12,409,138 outputs
Outputs from BMC Medical Informatics and Decision Making
#362
of 1,122 outputs
Outputs of similar age
#27,824
of 118,031 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#2
of 10 outputs
Altmetric has tracked 12,409,138 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 1,122 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 66% 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 118,031 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 75% of its contemporaries.
We're also able to compare this research output to 10 others from the same source and published within six weeks on either side of this one. This one has scored higher than 8 of them.