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Mendeley readers
Attention Score in Context
| Title |
Mining multi-item drug adverse effect associations in spontaneous reporting systems
|
|---|---|
| Published in |
BMC Bioinformatics, October 2010
|
| DOI | 10.1186/1471-2105-11-s9-s7 |
| Pubmed ID | |
| Authors |
Rave Harpaz, Herbert S Chase, Carol Friedman |
| Abstract |
Multi-item adverse drug event (ADE) associations are associations relating multiple drugs to possibly multiple adverse events. The current standard in pharmacovigilance is bivariate association analysis, where each single drug-adverse effect combination is studied separately. The importance and difficulty in the detection of multi-item ADE associations was noted in several prominent pharmacovigilance studies. In this paper we examine the application of a well established data mining method known as association rule mining, which we tailored to the above problem, and demonstrate its value. The method was applied to the FDAs spontaneous adverse event reporting system (AERS) with minimal restrictions and expectations on its output, an experiment that has not been previously done on the scale and generality proposed in this work. |
Mendeley readers
The data shown below were compiled from readership statistics for 134 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 3 | 2% |
| Japan | 2 | 1% |
| Brazil | 1 | <1% |
| Israel | 1 | <1% |
| Canada | 1 | <1% |
| Bulgaria | 1 | <1% |
| Finland | 1 | <1% |
| Slovenia | 1 | <1% |
| Unknown | 123 | 92% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 33 | 25% |
| Student > Master | 18 | 13% |
| Researcher | 15 | 11% |
| Student > Bachelor | 10 | 7% |
| Student > Doctoral Student | 9 | 7% |
| Other | 29 | 22% |
| Unknown | 20 | 15% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 32 | 24% |
| Medicine and Dentistry | 27 | 20% |
| Pharmacology, Toxicology and Pharmaceutical Science | 16 | 12% |
| Agricultural and Biological Sciences | 10 | 7% |
| Social Sciences | 4 | 3% |
| Other | 20 | 15% |
| Unknown | 25 | 19% |
Attention Score in Context
This research output has an Altmetric Attention Score of 12. 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 28 January 2022.
All research outputs
#2,489,366
of 23,006,268 outputs
Outputs from BMC Bioinformatics
#783
of 7,312 outputs
Outputs of similar age
#10,056
of 99,999 outputs
Outputs of similar age from BMC Bioinformatics
#3
of 56 outputs
Altmetric has tracked 23,006,268 research outputs across all sources so far. Compared to these this one has done well and is in the 88th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,312 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.4. This one has done well, scoring higher than 89% 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 99,999 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 89% of its contemporaries.
We're also able to compare this research output to 56 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 94% of its contemporaries.