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X Demographics
Mendeley readers
Attention Score in Context
| Title |
Enriching representation learning using 53 million patient notes through human phenotype ontology embedding
|
|---|---|
| Published in |
Artificial Intelligence in Medicine, February 2023
|
| DOI | 10.1016/j.artmed.2023.102523 |
| Pubmed ID | |
| Authors |
Maryam Daniali, Peter D Galer, David Lewis-Smith, Shridhar Parthasarathy, Edward Kim, Dario D Salvucci, Jeffrey M Miller, Scott Haag, Ingo Helbig |
X Demographics
The data shown below were collected from the profiles of 18 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 | 4 | 22% |
| Germany | 2 | 11% |
| Luxembourg | 1 | 6% |
| India | 1 | 6% |
| Australia | 1 | 6% |
| United Kingdom | 1 | 6% |
| Unknown | 8 | 44% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 44% |
| Scientists | 8 | 44% |
| Practitioners (doctors, other healthcare professionals) | 2 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 15 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 15 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Unspecified | 2 | 13% |
| Student > Bachelor | 2 | 13% |
| Researcher | 2 | 13% |
| Student > Doctoral Student | 1 | 7% |
| Other | 1 | 7% |
| Other | 4 | 27% |
| Unknown | 3 | 20% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 20% |
| Unspecified | 2 | 13% |
| Biochemistry, Genetics and Molecular Biology | 2 | 13% |
| Agricultural and Biological Sciences | 2 | 13% |
| Medicine and Dentistry | 1 | 7% |
| Other | 2 | 13% |
| Unknown | 3 | 20% |
Attention Score in Context
This research output has an Altmetric Attention Score of 34. 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 27 September 2023.
All research outputs
#1,203,628
of 25,738,558 outputs
Outputs from Artificial Intelligence in Medicine
#16
of 931 outputs
Outputs of similar age
#25,420
of 424,752 outputs
Outputs of similar age from Artificial Intelligence in Medicine
#2
of 22 outputs
Altmetric has tracked 25,738,558 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 931 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 98% 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 424,752 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 94% of its contemporaries.
We're also able to compare this research output to 22 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.