| Title |
Detection of cognitive impairment using a machine-learning algorithm
|
|---|---|
| Published in |
Neuropsychiatric Disease and Treatment, November 2018
|
| DOI | 10.2147/ndt.s171950 |
| Pubmed ID | |
| Authors |
Young Chul Youn, Seong Hye Choi, Hae-Won Shin, Ko Woon Kim, Jae-Won Jang, Jason J Jung, Ging-Yuek Robin Hsiung, SangYun Kim |
X Demographics
The data shown below were collected from the profiles of 12 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 Kingdom | 4 | 33% |
| United States | 3 | 25% |
| India | 2 | 17% |
| Unknown | 3 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 75% |
| Scientists | 3 | 25% |
Mendeley readers
The data shown below were compiled from readership statistics for 56 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 56 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 9 | 16% |
| Student > Ph. D. Student | 7 | 13% |
| Student > Bachelor | 6 | 11% |
| Other | 4 | 7% |
| Professor | 3 | 5% |
| Other | 9 | 16% |
| Unknown | 18 | 32% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 7 | 13% |
| Computer Science | 6 | 11% |
| Nursing and Health Professions | 6 | 11% |
| Psychology | 4 | 7% |
| Neuroscience | 3 | 5% |
| Other | 11 | 20% |
| Unknown | 19 | 34% |
Attention Score in Context
This research output has an Altmetric Attention Score of 16. 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 30 November 2018.
All research outputs
#2,287,025
of 25,385,509 outputs
Outputs from Neuropsychiatric Disease and Treatment
#286
of 3,131 outputs
Outputs of similar age
#47,795
of 363,432 outputs
Outputs of similar age from Neuropsychiatric Disease and Treatment
#4
of 108 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 90th percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 3,131 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 done particularly well, scoring higher than 90% 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 363,432 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 86% of its contemporaries.
We're also able to compare this research output to 108 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 96% of its contemporaries.