| Title |
Feasibility of Reidentifying Individuals in Large National Physical Activity Data Sets From Which Protected Health Information Has Been Removed With Use of Machine Learning
|
|---|---|
| Published in |
JAMA Network Open, December 2018
|
| DOI | 10.1001/jamanetworkopen.2018.6040 |
| Pubmed ID | |
| Authors |
Liangyuan Na, Cong Yang, Chi-Cheng Lo, Fangyuan Zhao, Yoshimi Fukuoka, Anil Aswani |
X Demographics
The data shown below were collected from the profiles of 232 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 | 90 | 39% |
| Canada | 14 | 6% |
| Australia | 10 | 4% |
| France | 8 | 3% |
| United Kingdom | 6 | 3% |
| Spain | 4 | 2% |
| Ireland | 3 | 1% |
| Netherlands | 2 | <1% |
| Switzerland | 2 | <1% |
| Other | 18 | 8% |
| Unknown | 75 | 32% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 134 | 58% |
| Scientists | 62 | 27% |
| Practitioners (doctors, other healthcare professionals) | 26 | 11% |
| Science communicators (journalists, bloggers, editors) | 10 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 185 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 185 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 29 | 16% |
| Student > Ph. D. Student | 23 | 12% |
| Student > Bachelor | 16 | 9% |
| Student > Master | 15 | 8% |
| Other | 14 | 8% |
| Other | 31 | 17% |
| Unknown | 57 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 34 | 18% |
| Computer Science | 19 | 10% |
| Social Sciences | 14 | 8% |
| Nursing and Health Professions | 10 | 5% |
| Engineering | 6 | 3% |
| Other | 36 | 19% |
| Unknown | 66 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 563. 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 14 November 2023.
All research outputs
#43,455
of 25,870,142 outputs
Outputs from JAMA Network Open
#474
of 10,022 outputs
Outputs of similar age
#820
of 446,604 outputs
Outputs of similar age from JAMA Network Open
#15
of 180 outputs
Altmetric has tracked 25,870,142 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 10,022 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 128.4. This one has done particularly well, scoring higher than 95% 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 446,604 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 99% of its contemporaries.
We're also able to compare this research output to 180 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 91% of its contemporaries.