| Title |
Using Machine Learning to Identify Patients at High Risk of Inappropriate Drug Dosing in Periods with Renal Dysfunction
|
|---|---|
| Published in |
Clinical Epidemiology, February 2022
|
| DOI | 10.2147/clep.s344435 |
| Pubmed ID | |
| Authors |
Benjamin Skov Kaas-Hansen, Cristina Leal Rodríguez, Davide Placido, Hans-Christian Thorsen-Meyer, Anna Pors Nielsen, Nicolas Dérian, Søren Brunak, Stig Ejdrup Andersen |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Switzerland | 1 | 20% |
| United States | 1 | 20% |
| Unknown | 3 | 60% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Practitioners (doctors, other healthcare professionals) | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 11 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 11 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 1 | 9% |
| Lecturer | 1 | 9% |
| Student > Master | 1 | 9% |
| Researcher | 1 | 9% |
| Professor > Associate Professor | 1 | 9% |
| Other | 1 | 9% |
| Unknown | 5 | 45% |
| Readers by discipline | Count | As % |
|---|---|---|
| Unspecified | 1 | 9% |
| Pharmacology, Toxicology and Pharmaceutical Science | 1 | 9% |
| Business, Management and Accounting | 1 | 9% |
| Nursing and Health Professions | 1 | 9% |
| Computer Science | 1 | 9% |
| Other | 1 | 9% |
| Unknown | 5 | 45% |
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 01 March 2022.
All research outputs
#15,879,822
of 25,584,565 outputs
Outputs from Clinical Epidemiology
#451
of 780 outputs
Outputs of similar age
#259,646
of 519,499 outputs
Outputs of similar age from Clinical Epidemiology
#17
of 28 outputs
Altmetric has tracked 25,584,565 research outputs across all sources so far. This one is in the 36th percentile – i.e., 36% of other outputs scored the same or lower than it.
So far Altmetric has tracked 780 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.7. This one is in the 40th percentile – i.e., 40% 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 519,499 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 28 others from the same source and published within six weeks on either side of this one. This one is in the 39th percentile – i.e., 39% of its contemporaries scored the same or lower than it.