↓ Skip to main content
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (82nd percentile)
  • Good Attention Score compared to outputs of the same age and source (78th percentile)

Mentioned by

news
1 news outlet
twitter
2 X users

Readers on

mendeley
47 Mendeley
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.
Article details
Title
A semi-mechanism approach based on MRI and proteomics for prediction of conversion from mild cognitive impairment to Alzheimer’s disease
Published in
Scientific Reports, June 2016
DOI 10.1038/srep26712
Pubmed ID
Authors
Abstract

Mild cognitive impairment (MCI) is a precursor phase of Alzheimer's disease (AD). As current treatments may be effective only at the early stages of AD, it is important to track MCI patients who will convert to AD. The aim of this study is to develop a high performance semi-mechanism based approach to predict the conversion from MCI to AD and improve our understanding of MCI-to-AD conversion mechanism. First, analysis of variance (ANOVA) test and lasso regression are employed to identify the markers related to the conversion. Then the Bayesian network based on selected markers is established to predict MCI-to-AD conversion. The structure of Bayesian network suggests that the conversion may start with fibrin clot formation, verbal memory impairment, eating pattern changing and hyperinsulinemia. The Bayesian network achieves a high 10-fold cross-validated prediction performance with 96% accuracy, 95% sensitivity, 65% specificity, area under the receiver operating characteristic curve of 0.82 on data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database. The semi-mechanism based approach provides not only high prediction performance but also clues of mechanism for MCI-to-AD conversion.

Login to access the Attention Digest and the Sentiment Analysis related to this output.

Timeline Attention over time Attention Score history
Login to access the full charts related to this output.
Activity
Login to access the full charts related to this output.
X Demographics

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.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 47 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Login to view Mendeley reader trends over time.

Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 2%
Unknown 46 98%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 8 17%
Student > Doctoral Student 7 15%
Student > Ph. D. Student 6 13%
Student > Master 5 11%
Other 3 6%
Other 6 13%
Unknown 12 26%
Readers by discipline
Readers by discipline Count As %
Medicine and Dentistry 6 13%
Neuroscience 6 13%
Agricultural and Biological Sciences 5 11%
Nursing and Health Professions 2 4%
Computer Science 2 4%
Other 9 19%
Unknown 17 36%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. 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 09 April 2018.
All research outputs
#3,171,539
of 22,877,793 outputs
Outputs from Scientific Reports
#26,756
of 123,594 outputs
Outputs of similar age
#57,461
of 341,020 outputs
Outputs of similar age from Scientific Reports
#772
of 3,551 outputs
Altmetric has tracked 22,877,793 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 123,594 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 18.2. This one has done well, scoring higher than 78% 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 341,020 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 82% of its contemporaries.
We're also able to compare this research output to 3,551 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 78% of its contemporaries.