| Title |
Machine Learning Assessment of Left Ventricular Diastolic Function Based on Electrocardiographic Features
|
|---|---|
| Published in |
JACC, August 2020
|
| DOI | 10.1016/j.jacc.2020.06.061 |
| Pubmed ID | |
| Authors |
Nobuyuki Kagiyama, Marco Piccirilli, Naveena Yanamala, Sirish Shrestha, Peter D. Farjo, Grace Casaclang-Verzosa, Wadea M. Tarhuni, Negin Nezarat, Matthew J. Budoff, Jagat Narula, Partho P. Sengupta |
X Demographics
The data shown below were collected from the profiles of 106 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 | 20 | 19% |
| Japan | 19 | 18% |
| United Kingdom | 4 | 4% |
| Switzerland | 2 | 2% |
| Italy | 2 | 2% |
| Germany | 2 | 2% |
| India | 1 | <1% |
| China | 1 | <1% |
| Argentina | 1 | <1% |
| Other | 11 | 10% |
| Unknown | 43 | 41% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 82 | 77% |
| Scientists | 17 | 16% |
| Practitioners (doctors, other healthcare professionals) | 6 | 6% |
| Science communicators (journalists, bloggers, editors) | 1 | <1% |
Mendeley readers
The data shown below were compiled from readership statistics for 119 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 119 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 14 | 12% |
| Other | 11 | 9% |
| Student > Ph. D. Student | 11 | 9% |
| Student > Master | 9 | 8% |
| Student > Bachelor | 7 | 6% |
| Other | 23 | 19% |
| Unknown | 44 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 30 | 25% |
| Computer Science | 9 | 8% |
| Engineering | 9 | 8% |
| Business, Management and Accounting | 2 | 2% |
| Agricultural and Biological Sciences | 2 | 2% |
| Other | 14 | 12% |
| Unknown | 53 | 45% |
Attention Score in Context
This research output has an Altmetric Attention Score of 86. 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 06 December 2023.
All research outputs
#513,163
of 25,927,633 outputs
Outputs from JACC
#1,287
of 17,009 outputs
Outputs of similar age
#15,486
of 429,328 outputs
Outputs of similar age from JACC
#43
of 249 outputs
Altmetric has tracked 25,927,633 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 98th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 17,009 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 30.2. This one has done particularly well, scoring higher than 92% 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 429,328 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 96% of its contemporaries.
We're also able to compare this research output to 249 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 82% of its contemporaries.