You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output.
Click here to find out more.
X Demographics
Mendeley readers
Attention Score in Context
| Title |
The application of unsupervised deep learning in predictive models using electronic health records
|
|---|---|
| Published in |
BMC Medical Research Methodology, February 2020
|
| DOI | 10.1186/s12874-020-00923-1 |
| Pubmed ID | |
| Authors |
Lei Wang, Liping Tong, Darcy Davis, Tim Arnold, Tina Esposito |
X Demographics
The data shown below were collected from the profiles of 11 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| India | 2 | 18% |
| Netherlands | 1 | 9% |
| Germany | 1 | 9% |
| Haiti | 1 | 9% |
| Brazil | 1 | 9% |
| France | 1 | 9% |
| Unknown | 4 | 36% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 82% |
| Scientists | 2 | 18% |
Mendeley readers
The data shown below were compiled from readership statistics for 52 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 52 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 10 | 19% |
| Student > Ph. D. Student | 7 | 13% |
| Student > Doctoral Student | 5 | 10% |
| Student > Bachelor | 4 | 8% |
| Lecturer | 3 | 6% |
| Other | 4 | 8% |
| Unknown | 19 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 8 | 15% |
| Medicine and Dentistry | 6 | 12% |
| Engineering | 4 | 8% |
| Mathematics | 2 | 4% |
| Pharmacology, Toxicology and Pharmaceutical Science | 2 | 4% |
| Other | 6 | 12% |
| Unknown | 24 | 46% |
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 26 April 2020.
All research outputs
#6,700,726
of 25,770,491 outputs
Outputs from BMC Medical Research Methodology
#926
of 2,317 outputs
Outputs of similar age
#119,378
of 384,817 outputs
Outputs of similar age from BMC Medical Research Methodology
#26
of 53 outputs
Altmetric has tracked 25,770,491 research outputs across all sources so far. This one has received more attention than most of these and is in the 73rd percentile.
So far Altmetric has tracked 2,317 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 10.6. This one has gotten more attention than average, scoring higher than 59% 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 384,817 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 53 others from the same source and published within six weeks on either side of this one. This one is in the 49th percentile – i.e., 49% of its contemporaries scored the same or lower than it.