| Title |
Rapid identification of COVID-19 severity in CT scans through classification of deep features
|
|---|---|
| Published in |
BioMedical Engineering OnLine, August 2020
|
| DOI | 10.1186/s12938-020-00807-x |
| Pubmed ID | |
| Authors |
Zekuan Yu, Xiaohu Li, Haitao Sun, Jian Wang, Tongtong Zhao, Hongyi Chen, Yichuan Ma, Shujin Zhu, Zongyu Xie |
X Demographics
The data shown below were collected from the profiles of 9 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 | 1 | 11% |
| Switzerland | 1 | 11% |
| Spain | 1 | 11% |
| South Africa | 1 | 11% |
| Singapore | 1 | 11% |
| Netherlands | 1 | 11% |
| Unknown | 3 | 33% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 89% |
| Practitioners (doctors, other healthcare professionals) | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 143 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 143 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 21 | 15% |
| Student > Master | 19 | 13% |
| Other | 9 | 6% |
| Researcher | 8 | 6% |
| Student > Ph. D. Student | 8 | 6% |
| Other | 20 | 14% |
| Unknown | 58 | 41% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 26 | 18% |
| Computer Science | 17 | 12% |
| Engineering | 14 | 10% |
| Nursing and Health Professions | 5 | 3% |
| Social Sciences | 3 | 2% |
| Other | 15 | 10% |
| Unknown | 63 | 44% |
Attention Score in Context
This research output has an Altmetric Attention Score of 4. 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 04 January 2021.
All research outputs
#7,409,087
of 23,511,526 outputs
Outputs from BioMedical Engineering OnLine
#200
of 837 outputs
Outputs of similar age
#156,137
of 399,981 outputs
Outputs of similar age from BioMedical Engineering OnLine
#4
of 9 outputs
Altmetric has tracked 23,511,526 research outputs across all sources so far. This one has received more attention than most of these and is in the 68th percentile.
So far Altmetric has tracked 837 research outputs from this source. They receive a mean Attention Score of 4.7. This one has done well, scoring higher than 75% 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 399,981 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 60% of its contemporaries.
We're also able to compare this research output to 9 others from the same source and published within six weeks on either side of this one. This one has scored higher than 5 of them.