| Title |
A Machine-Learning Algorithm to Optimise Automated Adverse Drug Reaction Detection from Clinical Coding
|
|---|---|
| Published in |
Drug Safety, February 2019
|
| DOI | 10.1007/s40264-018-00794-y |
| Pubmed ID | |
| Authors |
Christopher McMaster, David Liew, Claire Keith, Parnaz Aminian, Albert Frauman |
X Demographics
The data shown below were collected from the profiles of 27 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 | 6 | 22% |
| Australia | 4 | 15% |
| Canada | 1 | 4% |
| United Kingdom | 1 | 4% |
| Nepal | 1 | 4% |
| Unknown | 14 | 52% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 21 | 78% |
| Practitioners (doctors, other healthcare professionals) | 3 | 11% |
| Scientists | 2 | 7% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
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 % |
|---|---|---|
| Unknown | 41 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Doctoral Student | 4 | 10% |
| Student > Master | 4 | 10% |
| Researcher | 4 | 10% |
| Other | 3 | 7% |
| Student > Bachelor | 3 | 7% |
| Other | 4 | 10% |
| Unknown | 19 | 46% |
| Readers by discipline | Count | As % |
|---|---|---|
| Pharmacology, Toxicology and Pharmaceutical Science | 7 | 17% |
| Medicine and Dentistry | 5 | 12% |
| Nursing and Health Professions | 2 | 5% |
| Engineering | 2 | 5% |
| Biochemistry, Genetics and Molecular Biology | 1 | 2% |
| Other | 4 | 10% |
| Unknown | 20 | 49% |
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 21 May 2019.
All research outputs
#2,290,001
of 25,856,138 outputs
Outputs from Drug Safety
#236
of 1,874 outputs
Outputs of similar age
#52,541
of 448,688 outputs
Outputs of similar age from Drug Safety
#5
of 31 outputs
Altmetric has tracked 25,856,138 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 1,874 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 11.5. This one has done well, scoring higher than 87% 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 448,688 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 88% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 83% of its contemporaries.