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X Demographics
Mendeley readers
Attention Score in Context
| Title |
The use of machine learning in rare diseases: a scoping review
|
|---|---|
| Published in |
Orphanet Journal of Rare Diseases, June 2020
|
| DOI | 10.1186/s13023-020-01424-6 |
| Pubmed ID | |
| Authors |
Julia Schaefer, Moritz Lehne, Josef Schepers, Fabian Prasser, Sylvia Thun |
X Demographics
The data shown below were collected from the profiles of 24 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Greece | 2 | 8% |
| United States | 2 | 8% |
| United Kingdom | 2 | 8% |
| Spain | 1 | 4% |
| South Africa | 1 | 4% |
| Sri Lanka | 1 | 4% |
| Switzerland | 1 | 4% |
| United Arab Emirates | 1 | 4% |
| Germany | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 12 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 17 | 71% |
| Scientists | 6 | 25% |
| Practitioners (doctors, other healthcare professionals) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 198 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 198 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 29 | 15% |
| Student > Master | 22 | 11% |
| Student > Ph. D. Student | 16 | 8% |
| Other | 9 | 5% |
| Student > Doctoral Student | 8 | 4% |
| Other | 25 | 13% |
| Unknown | 89 | 45% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 23 | 12% |
| Computer Science | 20 | 10% |
| Biochemistry, Genetics and Molecular Biology | 19 | 10% |
| Engineering | 10 | 5% |
| Nursing and Health Professions | 5 | 3% |
| Other | 25 | 13% |
| Unknown | 96 | 48% |
Attention Score in Context
This research output has an Altmetric Attention Score of 30. 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 01 December 2022.
All research outputs
#1,295,543
of 25,301,208 outputs
Outputs from Orphanet Journal of Rare Diseases
#134
of 3,057 outputs
Outputs of similar age
#35,345
of 405,281 outputs
Outputs of similar age from Orphanet Journal of Rare Diseases
#4
of 55 outputs
Altmetric has tracked 25,301,208 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,057 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 8.2. This one has done particularly well, scoring higher than 95% 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 405,281 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 55 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.