| Title |
Assessment of a Machine Learning Model Applied to Harmonized Electronic Health Record Data for the Prediction of Incident Atrial Fibrillation
|
|---|---|
| Published in |
JAMA Network Open, January 2020
|
| DOI | 10.1001/jamanetworkopen.2019.19396 |
| Pubmed ID | |
| Authors |
Premanand Tiwari, Kathryn L. Colborn, Derek E. Smith, Fuyong Xing, Debashis Ghosh, Michael A. Rosenberg |
X Demographics
The data shown below were collected from the profiles of 26 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 | 10 | 38% |
| Switzerland | 3 | 12% |
| United Kingdom | 2 | 8% |
| Pakistan | 1 | 4% |
| Austria | 1 | 4% |
| Germany | 1 | 4% |
| Australia | 1 | 4% |
| France | 1 | 4% |
| Kenya | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 5 | 19% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 15 | 58% |
| Practitioners (doctors, other healthcare professionals) | 6 | 23% |
| Scientists | 4 | 15% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 131 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 131 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 17 | 13% |
| Student > Bachelor | 13 | 10% |
| Student > Ph. D. Student | 11 | 8% |
| Student > Master | 6 | 5% |
| Professor | 6 | 5% |
| Other | 25 | 19% |
| Unknown | 53 | 40% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 21 | 16% |
| Engineering | 14 | 11% |
| Computer Science | 13 | 10% |
| Unspecified | 4 | 3% |
| Mathematics | 4 | 3% |
| Other | 15 | 11% |
| Unknown | 60 | 46% |
Attention Score in Context
This research output has an Altmetric Attention Score of 24. 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 June 2021.
All research outputs
#1,421,644
of 23,796,227 outputs
Outputs from JAMA Network Open
#4,661
of 7,907 outputs
Outputs of similar age
#35,930
of 459,358 outputs
Outputs of similar age from JAMA Network Open
#179
of 292 outputs
Altmetric has tracked 23,796,227 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 94th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 7,907 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 129.8. This one is in the 41st percentile – i.e., 41% 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 459,358 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 92% of its contemporaries.
We're also able to compare this research output to 292 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.