| Title |
A deep learning model for real-time mortality prediction in critically ill children
|
|---|---|
| Published in |
Critical Care, August 2019
|
| DOI | 10.1186/s13054-019-2561-z |
| Pubmed ID | |
| Authors |
Soo Yeon Kim, Saehoon Kim, Joongbum Cho, Young Suh Kim, In Suk Sol, Youngchul Sung, Inhyeok Cho, Minseop Park, Haerin Jang, Yoon Hee Kim, Kyung Won Kim, Myung Hyun Sohn |
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 % |
|---|---|---|
| Nigeria | 2 | 15% |
| United States | 2 | 15% |
| Mexico | 1 | 8% |
| Malaysia | 1 | 8% |
| United Kingdom | 1 | 8% |
| Spain | 1 | 8% |
| Unknown | 5 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 10 | 77% |
| Practitioners (doctors, other healthcare professionals) | 2 | 15% |
| Science communicators (journalists, bloggers, editors) | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 162 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 162 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 29 | 18% |
| Student > Ph. D. Student | 17 | 10% |
| Other | 12 | 7% |
| Student > Master | 9 | 6% |
| Student > Doctoral Student | 9 | 6% |
| Other | 30 | 19% |
| Unknown | 56 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 39 | 24% |
| Computer Science | 14 | 9% |
| Engineering | 10 | 6% |
| Nursing and Health Professions | 7 | 4% |
| Business, Management and Accounting | 3 | 2% |
| Other | 19 | 12% |
| Unknown | 70 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 06 October 2019.
All research outputs
#5,171,345
of 25,571,620 outputs
Outputs from Critical Care
#3,365
of 6,588 outputs
Outputs of similar age
#93,623
of 354,152 outputs
Outputs of similar age from Critical Care
#52
of 80 outputs
Altmetric has tracked 25,571,620 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,588 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 20.8. This one is in the 48th percentile – i.e., 48% of its peers scored the same or lower than it.
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 354,152 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 73% of its contemporaries.
We're also able to compare this research output to 80 others from the same source and published within six weeks on either side of this one. This one is in the 36th percentile – i.e., 36% of its contemporaries scored the same or lower than it.