| Title |
Detection of cognitive impairment using a machine-learning algorithm [Corrigendum]
|
|---|---|
| Published in |
Neuropsychiatric Disease and Treatment, May 2019
|
| DOI | 10.2147/ndt.s212732 |
| 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 |
Attention Score in Context
This research output has an Altmetric Attention Score of 7. 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 31 May 2019.
All research outputs
#4,839,027
of 25,385,509 outputs
Outputs from Neuropsychiatric Disease and Treatment
#684
of 3,131 outputs
Outputs of similar age
#93,071
of 363,254 outputs
Outputs of similar age from Neuropsychiatric Disease and Treatment
#15
of 76 outputs
Altmetric has tracked 25,385,509 research outputs across all sources so far. Compared to these this one has done well and is in the 79th percentile: it's in the top 25% 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 well, scoring higher than 76% 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 363,254 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 73% of its contemporaries.
We're also able to compare this research output to 76 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 80% of its contemporaries.