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 |
A clinical text classification paradigm using weak supervision and deep representation
|
|---|---|
| Published in |
BMC Medical Informatics and Decision Making, January 2019
|
| DOI | 10.1186/s12911-018-0723-6 |
| Pubmed ID | |
| Authors |
Yanshan Wang, Sunghwan Sohn, Sijia Liu, Feichen Shen, Liwei Wang, Elizabeth J. Atkinson, Shreyasee Amin, Hongfang Liu |
X Demographics
The data shown below were collected from the profiles of 5 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 | 80% |
| France | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 3 | 60% |
| Scientists | 2 | 40% |
Mendeley readers
The data shown below were compiled from readership statistics for 102 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 102 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 12 | 12% |
| Student > Bachelor | 12 | 12% |
| Student > Master | 9 | 9% |
| Researcher | 8 | 8% |
| Other | 6 | 6% |
| Other | 19 | 19% |
| Unknown | 36 | 35% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 27 | 26% |
| Medicine and Dentistry | 10 | 10% |
| Engineering | 8 | 8% |
| Biochemistry, Genetics and Molecular Biology | 3 | 3% |
| Unspecified | 3 | 3% |
| Other | 10 | 10% |
| Unknown | 41 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 16 November 2019.
All research outputs
#7,243,755
of 23,122,481 outputs
Outputs from BMC Medical Informatics and Decision Making
#720
of 2,013 outputs
Outputs of similar age
#149,361
of 437,003 outputs
Outputs of similar age from BMC Medical Informatics and Decision Making
#20
of 51 outputs
Altmetric has tracked 23,122,481 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 2,013 research outputs from this source. They receive a mean Attention Score of 4.9. This one has gotten more attention than average, scoring higher than 63% 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 437,003 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 65% of its contemporaries.
We're also able to compare this research output to 51 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 60% of its contemporaries.