| Title |
Enabling realistic health data re-identification risk assessment through adversarial modeling
|
|---|---|
| Published in |
Journal of the American Medical Informatics Association, January 2021
|
| DOI | 10.1093/jamia/ocaa327 |
| Pubmed ID | |
| Authors |
Weiyi Xia, Yongtai Liu, Zhiyu Wan, Yevgeniy Vorobeychik, Murat Kantacioglu, Steve Nyemba, Ellen Wright Clayton, Bradley A Malin |
X Demographics
The data shown below were collected from the profiles of 2 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 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 1 | 50% |
| Members of the public | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 22 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 4 | 18% |
| Student > Bachelor | 3 | 14% |
| Student > Ph. D. Student | 2 | 9% |
| Professor > Associate Professor | 2 | 9% |
| Researcher | 2 | 9% |
| Other | 1 | 5% |
| Unknown | 8 | 36% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 3 | 14% |
| Medicine and Dentistry | 3 | 14% |
| Engineering | 2 | 9% |
| Business, Management and Accounting | 1 | 5% |
| Nursing and Health Professions | 1 | 5% |
| Other | 4 | 18% |
| Unknown | 8 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 2. 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 13 April 2021.
All research outputs
#15,024,725
of 23,308,124 outputs
Outputs from Journal of the American Medical Informatics Association
#2,579
of 3,106 outputs
Outputs of similar age
#283,644
of 502,964 outputs
Outputs of similar age from Journal of the American Medical Informatics Association
#70
of 83 outputs
Altmetric has tracked 23,308,124 research outputs across all sources so far. This one is in the 34th percentile – i.e., 34% of other outputs scored the same or lower than it.
So far Altmetric has tracked 3,106 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 14.2. This one is in the 16th percentile – i.e., 16% of its peers scored the same or lower than it.
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 502,964 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 42nd percentile – i.e., 42% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 83 others from the same source and published within six weeks on either side of this one. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.