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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Machine Learning-Based Prediction of Hospital Mortality in Patients With Postoperative Sepsis
|
|---|---|
| Published in |
Frontiers in Medicine, August 2020
|
| DOI | 10.3389/fmed.2020.00445 |
| Pubmed ID | |
| Authors |
Ren-qi Yao, Xin Jin, Guo-wei Wang, Yue Yu, Guo-sheng Wu, Yi-bing Zhu, Lin Li, Yu-xuan Li, Peng-yue Zhao, Sheng-yu Zhu, Zhao-fan Xia, Chao Ren, Yong-ming Yao |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Austria | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 62 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 62 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 8 | 13% |
| Student > Ph. D. Student | 6 | 10% |
| Student > Bachelor | 6 | 10% |
| Student > Master | 5 | 8% |
| Professor | 2 | 3% |
| Other | 5 | 8% |
| Unknown | 30 | 48% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 11 | 18% |
| Computer Science | 7 | 11% |
| Engineering | 3 | 5% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 3% |
| Neuroscience | 2 | 3% |
| Other | 5 | 8% |
| Unknown | 32 | 52% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 26 August 2020.
All research outputs
#18,741,816
of 23,232,430 outputs
Outputs from Frontiers in Medicine
#4,122
of 5,932 outputs
Outputs of similar age
#300,579
of 398,751 outputs
Outputs of similar age from Frontiers in Medicine
#162
of 210 outputs
Altmetric has tracked 23,232,430 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 5,932 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 13.5. This one is in the 13th percentile – i.e., 13% 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 398,751 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 14th percentile – i.e., 14% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 210 others from the same source and published within six weeks on either side of this one. This one is in the 4th percentile – i.e., 4% of its contemporaries scored the same or lower than it.