| Title |
Identifying Ventricular Arrhythmias and Their Predictors by Applying Machine Learning Methods to Electronic Health Records in Patients With Hypertrophic Cardiomyopathy (HCM-VAr-Risk Model)
|
|---|---|
| Published in |
American Journal of Cardiology, February 2019
|
| DOI | 10.1016/j.amjcard.2019.02.022 |
| Pubmed ID | |
| Authors |
Moumita Bhattacharya, Dai-Yin Lu, Shibani M Kudchadkar, Gabriela Villarreal Greenland, Prasanth Lingamaneni, Celia P Corona-Villalobos, Yufan Guan, Joseph E Marine, Jeffrey E Olgin, Stefan Zimmerman, Theodore P Abraham, Hagit Shatkay, Maria Roselle Abraham |
X Demographics
The data shown below were collected from the profiles of 8 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 | 2 | 25% |
| Spain | 2 | 25% |
| Netherlands | 1 | 13% |
| Unknown | 3 | 38% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 6 | 75% |
| Scientists | 1 | 13% |
| Practitioners (doctors, other healthcare professionals) | 1 | 13% |
Mendeley readers
The data shown below were compiled from readership statistics for 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 118 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 26 | 22% |
| Student > Master | 12 | 10% |
| Researcher | 11 | 9% |
| Student > Bachelor | 11 | 9% |
| Student > Doctoral Student | 9 | 8% |
| Other | 13 | 11% |
| Unknown | 36 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 28 | 24% |
| Computer Science | 14 | 12% |
| Engineering | 12 | 10% |
| Business, Management and Accounting | 8 | 7% |
| Nursing and Health Professions | 3 | 3% |
| Other | 12 | 10% |
| Unknown | 41 | 35% |
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 30 May 2021.
All research outputs
#6,621,063
of 25,756,911 outputs
Outputs from American Journal of Cardiology
#2,847
of 10,247 outputs
Outputs of similar age
#117,033
of 368,626 outputs
Outputs of similar age from American Journal of Cardiology
#33
of 111 outputs
Altmetric has tracked 25,756,911 research outputs across all sources so far. This one has received more attention than most of these and is in the 74th percentile.
So far Altmetric has tracked 10,247 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one has gotten more attention than average, scoring higher than 72% 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 368,626 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 111 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 69% of its contemporaries.