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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Large-scale combining signals from both biomedical literature and the FDA Adverse Event Reporting System (FAERS) to improve post-marketing drug safety signal detection
|
|---|---|
| Published in |
BMC Bioinformatics, January 2014
|
| DOI | 10.1186/1471-2105-15-17 |
| Pubmed ID | |
| Authors |
Rong Xu, QuanQiu Wang |
| Abstract |
Independent data sources can be used to augment post-marketing drug safety signal detection. The vast amount of publicly available biomedical literature contains rich side effect information for drugs at all clinical stages. In this study, we present a large-scale signal boosting approach that combines over 4 million records in the US Food and Drug Administration (FDA) Adverse Event Reporting System (FAERS) and over 21 million biomedical articles. |
X Demographics
The data shown below were collected from the profiles of 8 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 States | 3 | 38% |
| Norway | 1 | 13% |
| Unknown | 4 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 63% |
| Practitioners (doctors, other healthcare professionals) | 2 | 25% |
| Scientists | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 2% |
| Korea, Republic of | 1 | 1% |
| Hong Kong | 1 | 1% |
| Netherlands | 1 | 1% |
| Canada | 1 | 1% |
| Finland | 1 | 1% |
| Japan | 1 | 1% |
| Spain | 1 | 1% |
| Unknown | 85 | 90% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 17 | 18% |
| Researcher | 15 | 16% |
| Student > Master | 14 | 15% |
| Student > Doctoral Student | 7 | 7% |
| Professor > Associate Professor | 7 | 7% |
| Other | 17 | 18% |
| Unknown | 17 | 18% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 20 | 21% |
| Computer Science | 19 | 20% |
| Agricultural and Biological Sciences | 11 | 12% |
| Pharmacology, Toxicology and Pharmaceutical Science | 6 | 6% |
| Nursing and Health Professions | 4 | 4% |
| Other | 11 | 12% |
| Unknown | 23 | 24% |
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 22 August 2017.
All research outputs
#6,782,242
of 23,881,329 outputs
Outputs from BMC Bioinformatics
#2,540
of 7,454 outputs
Outputs of similar age
#85,193
of 336,258 outputs
Outputs of similar age from BMC Bioinformatics
#30
of 102 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. This one has received more attention than most of these and is in the 70th percentile.
So far Altmetric has tracked 7,454 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.5. This one has gotten more attention than average, scoring higher than 64% 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 336,258 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 73% of its contemporaries.
We're also able to compare this research output to 102 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 70% of its contemporaries.