The data shown below were collected from the profiles of 17 X users who shared this research output. Click here to find out more about how the information was compiled.
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X Demographics
Mendeley readers
Attention Score in Context
| Title |
The Agile Deployment of Machine Learning Models in Healthcare
|
|---|---|
| Published in |
Frontiers in Big Data, January 2019
|
| DOI | 10.3389/fdata.2018.00007 |
| Pubmed ID | |
| Authors |
Stuart Jackson, Maha Yaqub, Cheng-Xi Li |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 2 | 12% |
| United States | 2 | 12% |
| Belgium | 2 | 12% |
| Saudi Arabia | 1 | 6% |
| Lithuania | 1 | 6% |
| Canada | 1 | 6% |
| Unknown | 8 | 47% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 12 | 71% |
| Scientists | 3 | 18% |
| Practitioners (doctors, other healthcare professionals) | 2 | 12% |
Mendeley readers
The data shown below were compiled from readership statistics for 75 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 75 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 11 | 15% |
| Student > Doctoral Student | 7 | 9% |
| Researcher | 7 | 9% |
| Student > Master | 6 | 8% |
| Student > Ph. D. Student | 5 | 7% |
| Other | 8 | 11% |
| Unknown | 31 | 41% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 21 | 28% |
| Medicine and Dentistry | 7 | 9% |
| Engineering | 6 | 8% |
| Business, Management and Accounting | 2 | 3% |
| Design | 2 | 3% |
| Other | 7 | 9% |
| Unknown | 30 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 11. 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 13 July 2022.
All research outputs
#3,475,298
of 26,555,952 outputs
Outputs from Frontiers in Big Data
#45
of 505 outputs
Outputs of similar age
#74,143
of 452,425 outputs
Outputs of similar age from Frontiers in Big Data
#1
of 2 outputs
Altmetric has tracked 26,555,952 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 505 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 5.9. This one has done particularly well, scoring higher than 90% 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 452,425 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 83% of its contemporaries.
We're also able to compare this research output to 2 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them