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X Demographics
Mendeley readers
Attention Score in Context
| Title |
A Machine Learning Approach for the Differential Diagnosis of Alzheimer and Vascular Dementia Fed by MRI Selected Features
|
|---|---|
| Published in |
Frontiers in Neuroinformatics, June 2020
|
| DOI | 10.3389/fninf.2020.00025 |
| Pubmed ID | |
| Authors |
Gloria Castellazzi, Maria Giovanna Cuzzoni, Matteo Cotta Ramusino, Daniele Martinelli, Federica Denaro, Antonio Ricciardi, Paolo Vitali, Nicoletta Anzalone, Sara Bernini, Fulvia Palesi, Elena Sinforiani, Alfredo Costa, Giuseppe Micieli, Egidio D'Angelo, Giovanni Magenes, Claudia A. M. Gandini Wheeler-Kingshott |
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 | 1 | 8% |
| Brazil | 1 | 8% |
| Japan | 1 | 8% |
| France | 1 | 8% |
| Chile | 1 | 8% |
| Switzerland | 1 | 8% |
| Unknown | 6 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 11 | 92% |
| Scientists | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 153 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 153 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 20 | 13% |
| Researcher | 14 | 9% |
| Student > Bachelor | 11 | 7% |
| Lecturer | 8 | 5% |
| Student > Doctoral Student | 7 | 5% |
| Other | 19 | 12% |
| Unknown | 74 | 48% |
| Readers by discipline | Count | As % |
|---|---|---|
| Engineering | 19 | 12% |
| Computer Science | 19 | 12% |
| Medicine and Dentistry | 8 | 5% |
| Neuroscience | 7 | 5% |
| Unspecified | 5 | 3% |
| Other | 11 | 7% |
| Unknown | 84 | 55% |
Attention Score in Context
This research output has an Altmetric Attention Score of 33. 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 October 2022.
All research outputs
#1,219,239
of 25,622,179 outputs
Outputs from Frontiers in Neuroinformatics
#26
of 844 outputs
Outputs of similar age
#35,795
of 434,856 outputs
Outputs of similar age from Frontiers in Neuroinformatics
#1
of 15 outputs
Altmetric has tracked 25,622,179 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 95th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 844 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.7. This one has done particularly well, scoring higher than 97% 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 434,856 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 91% of its contemporaries.
We're also able to compare this research output to 15 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 99% of its contemporaries.