The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
Using Machine Learning to Predict Mortality for COVID-19 Patients on Day 0 in the ICU
|
|---|---|
| Published in |
Frontiers in Digital Health, January 2022
|
| DOI | 10.3389/fdgth.2021.681608 |
| Pubmed ID | |
| Authors |
Elham Jamshidi, Amirhossein Asgary, Nader Tavakoli, Alireza Zali, Soroush Setareh, Hadi Esmaily, Seyed Hamid Jamaldini, Amir Daaee, Amirhesam Babajani, Mohammad Ali Sendani Kashi, Masoud Jamshidi, Sahand Jamal Rahi, Nahal Mansouri |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 33% |
| Unknown | 2 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 65 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 65 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 8 | 12% |
| Student > Bachelor | 7 | 11% |
| Student > Ph. D. Student | 5 | 8% |
| Other | 4 | 6% |
| Researcher | 4 | 6% |
| Other | 7 | 11% |
| Unknown | 30 | 46% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 9 | 14% |
| Nursing and Health Professions | 8 | 12% |
| Computer Science | 4 | 6% |
| Biochemistry, Genetics and Molecular Biology | 2 | 3% |
| Environmental Science | 2 | 3% |
| Other | 9 | 14% |
| Unknown | 31 | 48% |
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 10 February 2023.
All research outputs
#3,099,063
of 23,742,253 outputs
Outputs from Frontiers in Digital Health
#97
of 621 outputs
Outputs of similar age
#76,676
of 518,328 outputs
Outputs of similar age from Frontiers in Digital Health
#9
of 60 outputs
Altmetric has tracked 23,742,253 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 621 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.9. This one has done well, scoring higher than 84% 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 518,328 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 84% of its contemporaries.
We're also able to compare this research output to 60 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 86% of its contemporaries.