| Title |
Ethical considerations about artificial intelligence for prognostication in intensive care
|
|---|---|
| Published in |
Intensive Care Medicine Experimental, December 2019
|
| DOI | 10.1186/s40635-019-0286-6 |
| Pubmed ID | |
| Authors |
Michael Beil, Ingo Proft, Daniel van Heerden, Sigal Sviri, Peter Vernon van Heerden |
X Demographics
The data shown below were collected from the profiles of 23 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 | 5 | 22% |
| United States | 2 | 9% |
| Spain | 1 | 4% |
| Malaysia | 1 | 4% |
| Netherlands | 1 | 4% |
| Brazil | 1 | 4% |
| Italy | 1 | 4% |
| France | 1 | 4% |
| Germany | 1 | 4% |
| Other | 3 | 13% |
| Unknown | 6 | 26% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 43% |
| Practitioners (doctors, other healthcare professionals) | 7 | 30% |
| Scientists | 5 | 22% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 153 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 17 | 11% |
| Student > Bachelor | 17 | 11% |
| Student > Ph. D. Student | 16 | 10% |
| Student > Doctoral Student | 11 | 7% |
| Other | 6 | 4% |
| Other | 24 | 16% |
| Unknown | 62 | 41% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 22 | 14% |
| Computer Science | 13 | 8% |
| Engineering | 10 | 7% |
| Nursing and Health Professions | 8 | 5% |
| Biochemistry, Genetics and Molecular Biology | 6 | 4% |
| Other | 29 | 19% |
| Unknown | 65 | 42% |
Attention Score in Context
This research output has an Altmetric Attention Score of 13. 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 18 January 2020.
All research outputs
#2,510,447
of 23,182,015 outputs
Outputs from Intensive Care Medicine Experimental
#65
of 455 outputs
Outputs of similar age
#62,349
of 459,268 outputs
Outputs of similar age from Intensive Care Medicine Experimental
#2
of 16 outputs
Altmetric has tracked 23,182,015 research outputs across all sources so far. Compared to these this one has done well and is in the 89th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 455 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one has done well, scoring higher than 85% 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 459,268 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 86% of its contemporaries.
We're also able to compare this research output to 16 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 87% of its contemporaries.