| Title |
Towards a decision support tool for intensive care discharge: machine learning algorithm development using electronic healthcare data from MIMIC-III and Bristol, UK
|
|---|---|
| Published in |
BMJ Open, March 2019
|
| DOI | 10.1136/bmjopen-2018-025925 |
| Pubmed ID | |
| Authors |
Christopher J McWilliams, Daniel J Lawson, Raul Santos-Rodriguez, Iain D Gilchrist, Alan Champneys, Timothy H Gould, Mathew Jc Thomas, Christopher P Bourdeaux |
X Demographics
The data shown below were collected from the profiles of 13 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 | 9 | 69% |
| Namibia | 1 | 8% |
| United States | 1 | 8% |
| Unknown | 2 | 15% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 69% |
| Practitioners (doctors, other healthcare professionals) | 2 | 15% |
| Scientists | 2 | 15% |
Mendeley readers
The data shown below were compiled from readership statistics for 154 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 154 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 28 | 18% |
| Researcher | 19 | 12% |
| Student > Master | 17 | 11% |
| Student > Bachelor | 15 | 10% |
| Student > Doctoral Student | 6 | 4% |
| Other | 13 | 8% |
| Unknown | 56 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 22 | 14% |
| Computer Science | 19 | 12% |
| Engineering | 12 | 8% |
| Nursing and Health Professions | 8 | 5% |
| Business, Management and Accounting | 5 | 3% |
| Other | 28 | 18% |
| Unknown | 60 | 39% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 15 June 2021.
All research outputs
#3,556,875
of 25,385,509 outputs
Outputs from BMJ Open
#6,827
of 25,597 outputs
Outputs of similar age
#76,053
of 365,867 outputs
Outputs of similar age from BMJ Open
#294
of 777 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 25,597 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one has gotten more attention than average, scoring higher than 73% 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 365,867 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 79% of its contemporaries.
We're also able to compare this research output to 777 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 62% of its contemporaries.