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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Using machine learning methods to predict in-hospital mortality of sepsis patients in the ICU
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, October 2020
|
| DOI | 10.1186/s12911-020-01271-2 |
| Pubmed ID | |
| Authors |
Guilan Kong, Ke Lin, Yonghua Hu |
X Demographics
The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Spain | 3 | 33% |
| United States | 1 | 11% |
| Unknown | 5 | 56% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 67% |
| Practitioners (doctors, other healthcare professionals) | 2 | 22% |
| Scientists | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 183 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 183 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 36 | 20% |
| Student > Ph. D. Student | 16 | 9% |
| Student > Master | 15 | 8% |
| Student > Bachelor | 12 | 7% |
| Researcher | 9 | 5% |
| Other | 19 | 10% |
| Unknown | 76 | 42% |
| Readers by discipline | Count | As % |
|---|---|---|
| Unspecified | 36 | 20% |
| Computer Science | 20 | 11% |
| Medicine and Dentistry | 19 | 10% |
| Engineering | 6 | 3% |
| Nursing and Health Professions | 4 | 2% |
| Other | 16 | 9% |
| Unknown | 82 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 8. 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 11 March 2022.
All research outputs
#4,138,443
of 23,881,329 outputs
Outputs from BMC Medical Informatics and Decision Making
#336
of 2,030 outputs
Outputs of similar age
#98,834
of 414,634 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#8
of 49 outputs
Altmetric has tracked 23,881,329 research outputs across all sources so far. Compared to these this one has done well and is in the 82nd percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,030 research outputs from this source. They receive a mean Attention Score of 4.9. This one has done well, scoring higher than 83% 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 414,634 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 76% of its contemporaries.
We're also able to compare this research output to 49 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 85% of its contemporaries.