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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A unified machine learning approach to time series forecasting applied to demand at emergency departments
|
|---|---|
| Published in |
BMC Emergency Medicine, January 2021
|
| DOI | 10.1186/s12873-020-00395-y |
| Pubmed ID | |
| Authors |
Michaela A.C. Vollmer, Ben Glampson, Thomas Mellan, Swapnil Mishra, Luca Mercuri, Ceire Costello, Robert Klaber, Graham Cooke, Seth Flaxman, Samir Bhatt |
X Demographics
The data shown below were collected from the profiles of 9 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 Kingdom | 1 | 11% |
| Denmark | 1 | 11% |
| Unknown | 7 | 78% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 67% |
| Scientists | 2 | 22% |
| Practitioners (doctors, other healthcare professionals) | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 74 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 74 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 10 | 14% |
| Student > Ph. D. Student | 7 | 9% |
| Researcher | 6 | 8% |
| Student > Postgraduate | 3 | 4% |
| Professor | 3 | 4% |
| Other | 12 | 16% |
| Unknown | 33 | 45% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 9 | 12% |
| Computer Science | 7 | 9% |
| Mathematics | 5 | 7% |
| Business, Management and Accounting | 5 | 7% |
| Medicine and Dentistry | 4 | 5% |
| Other | 11 | 15% |
| Unknown | 33 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 20 January 2021.
All research outputs
#6,108,720
of 24,067,703 outputs
Outputs from BMC Emergency Medicine
#253
of 802 outputs
Outputs of similar age
#144,795
of 508,974 outputs
Outputs of similar age from BMC Emergency Medicine
#7
of 29 outputs
Altmetric has tracked 24,067,703 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 802 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one has gotten more attention than average, scoring higher than 68% 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 508,974 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 71% of its contemporaries.
We're also able to compare this research output to 29 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 79% of its contemporaries.