| Title |
Model-Based and Model-Free Techniques for Amyotrophic Lateral Sclerosis Diagnostic Prediction and Patient Clustering
|
|---|---|
| Published in |
Neuroinformatics, November 2018
|
| DOI | 10.1007/s12021-018-9406-9 |
| Pubmed ID | |
| Authors |
Ming Tang, Chao Gao, Stephen A. Goutman, Alexandr Kalinin, Bhramar Mukherjee, Yuanfang Guan, Ivo D. Dinov |
X Demographics
The data shown below were collected from the profiles of 13 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 | 5 | 38% |
| Australia | 2 | 15% |
| India | 1 | 8% |
| Italy | 1 | 8% |
| Unknown | 4 | 31% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 69% |
| Scientists | 3 | 23% |
| Practitioners (doctors, other healthcare professionals) | 1 | 8% |
Mendeley readers
The data shown below were compiled from readership statistics for 68 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 68 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 8 | 12% |
| Student > Master | 7 | 10% |
| Researcher | 6 | 9% |
| Student > Ph. D. Student | 5 | 7% |
| Student > Doctoral Student | 4 | 6% |
| Other | 9 | 13% |
| Unknown | 29 | 43% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 10 | 15% |
| Engineering | 5 | 7% |
| Computer Science | 5 | 7% |
| Agricultural and Biological Sciences | 5 | 7% |
| Unspecified | 3 | 4% |
| Other | 11 | 16% |
| Unknown | 29 | 43% |
Attention Score in Context
This research output has an Altmetric Attention Score of 18. 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 27 February 2024.
All research outputs
#2,058,599
of 25,440,205 outputs
Outputs from Neuroinformatics
#15
of 425 outputs
Outputs of similar age
#45,431
of 446,663 outputs
Outputs of similar age from Neuroinformatics
#1
of 7 outputs
Altmetric has tracked 25,440,205 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 425 research outputs from this source. They receive a mean Attention Score of 4.8. This one has done particularly well, scoring higher than 96% 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 446,663 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 89% of its contemporaries.
We're also able to compare this research output to 7 others from the same source and published within six weeks on either side of this one. This one has scored higher than all of them