| Title |
A Gradient Boosting Machine Learning Model for Predicting Early Mortality in the Emergency Department Triage: Devising a Nine-Point Triage Score
|
|---|---|
| Published in |
Journal of General Internal Medicine, November 2019
|
| DOI | 10.1007/s11606-019-05512-7 |
| Pubmed ID | |
| Authors |
Maximiliano Klug, Yiftach Barash, Sigalit Bechler, Yehezkel S. Resheff, Talia Tron, Avi Ironi, Shelly Soffer, Eyal Zimlichman, Eyal Klang |
X Demographics
The data shown below were collected from the profiles of 4 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 States | 1 | 25% |
| Unknown | 3 | 75% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 2 | 50% |
| Science communicators (journalists, bloggers, editors) | 1 | 25% |
| Practitioners (doctors, other healthcare professionals) | 1 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 112 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 112 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 12 | 11% |
| Researcher | 9 | 8% |
| Student > Ph. D. Student | 8 | 7% |
| Lecturer | 6 | 5% |
| Student > Doctoral Student | 6 | 5% |
| Other | 13 | 12% |
| Unknown | 58 | 52% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 17 | 15% |
| Computer Science | 9 | 8% |
| Nursing and Health Professions | 7 | 6% |
| Engineering | 5 | 4% |
| Business, Management and Accounting | 3 | 3% |
| Other | 12 | 11% |
| Unknown | 59 | 53% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 25 November 2019.
All research outputs
#14,991,509
of 23,911,072 outputs
Outputs from Journal of General Internal Medicine
#5,552
of 7,806 outputs
Outputs of similar age
#202,650
of 365,979 outputs
Outputs of similar age from Journal of General Internal Medicine
#154
of 208 outputs
Altmetric has tracked 23,911,072 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 7,806 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 21.8. This one is in the 28th percentile – i.e., 28% 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 365,979 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 44th percentile – i.e., 44% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 208 others from the same source and published within six weeks on either side of this one. This one is in the 25th percentile – i.e., 25% of its contemporaries scored the same or lower than it.