The data shown below were collected from the profiles of 7 X users who shared this research output. Click here to find out more about how the information was compiled.
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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Machine Learning Patient-Specific Prediction of Heart Failure Hospitalization Using Cardiac MRI-Based Phenotype and Electronic Health Information
|
|---|---|
| Published in |
Frontiers in Cardiovascular Medicine, June 2022
|
| DOI | 10.3389/fcvm.2022.890904 |
| Pubmed ID | |
| Authors |
Aidan K. Cornhill, Steven Dykstra, Alessandro Satriano, Dina Labib, Yoko Mikami, Jacqueline Flewitt, Easter Prosio, Sandra Rivest, Rosa Sandonato, Andrew G. Howarth, Carmen Lydell, Cathy A. Eastwood, Hude Quan, Nowell Fine, Joon Lee, James A. White |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Canada | 2 | 29% |
| Switzerland | 1 | 14% |
| Unknown | 4 | 57% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 5 | 71% |
| Science communicators (journalists, bloggers, editors) | 1 | 14% |
| Scientists | 1 | 14% |
Mendeley readers
The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 26 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 5 | 19% |
| Unspecified | 2 | 8% |
| Other | 2 | 8% |
| Student > Ph. D. Student | 2 | 8% |
| Student > Bachelor | 1 | 4% |
| Other | 2 | 8% |
| Unknown | 12 | 46% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 6 | 23% |
| Business, Management and Accounting | 2 | 8% |
| Unspecified | 2 | 8% |
| Engineering | 2 | 8% |
| Computer Science | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 13 | 50% |
Attention Score in Context
This research output has an Altmetric Attention Score of 6. 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 12 August 2022.
All research outputs
#5,716,842
of 23,081,466 outputs
Outputs from Frontiers in Cardiovascular Medicine
#860
of 6,995 outputs
Outputs of similar age
#102,206
of 413,283 outputs
Outputs of similar age from Frontiers in Cardiovascular Medicine
#92
of 928 outputs
Altmetric has tracked 23,081,466 research outputs across all sources so far. Compared to these this one has done well and is in the 75th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 6,995 research outputs from this source. They receive a mean Attention Score of 4.2. This one has done well, scoring higher than 87% 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 413,283 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 75% of its contemporaries.
We're also able to compare this research output to 928 others from the same source and published within six weeks on either side of this one. This one has done particularly well, scoring higher than 90% of its contemporaries.