Title |
Development and Validation of a Deep Learning-Based Model Using Computed Tomography Imaging for Predicting Disease Severity of Coronavirus Disease 2019
|
---|---|
Published in |
Frontiers in Bioengineering and Biotechnology, July 2020
|
DOI | 10.3389/fbioe.2020.00898 |
Pubmed ID | |
Authors |
Lu-shan Xiao, Pu Li, Fenglong Sun, Yanpei Zhang, Chenghai Xu, Hongbo Zhu, Feng-Qin Cai, Yu-Lin He, Wen-Feng Zhang, Si-Cong Ma, Chenyi Hu, Mengchun Gong, Li Liu, Wenzhao Shi, Hong Zhu |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Comoros | 1 | 25% |
United States | 1 | 25% |
Switzerland | 1 | 25% |
Unknown | 1 | 25% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 118 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 118 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Other | 14 | 12% |
Student > Master | 14 | 12% |
Student > Bachelor | 12 | 10% |
Researcher | 10 | 8% |
Student > Doctoral Student | 8 | 7% |
Other | 22 | 19% |
Unknown | 38 | 32% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 19 | 16% |
Computer Science | 14 | 12% |
Engineering | 13 | 11% |
Nursing and Health Professions | 4 | 3% |
Physics and Astronomy | 3 | 3% |
Other | 22 | 19% |
Unknown | 43 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 28 August 2020.
All research outputs
#15,620,220
of 23,225,652 outputs
Outputs from Frontiers in Bioengineering and Biotechnology
#2,695
of 6,900 outputs
Outputs of similar age
#248,612
of 398,138 outputs
Outputs of similar age from Frontiers in Bioengineering and Biotechnology
#194
of 465 outputs
Altmetric has tracked 23,225,652 research outputs across all sources so far. This one is in the 22nd percentile – i.e., 22% of other outputs scored the same or lower than it.
So far Altmetric has tracked 6,900 research outputs from this source. They receive a mean Attention Score of 3.5. This one has gotten more attention than average, scoring higher than 56% 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 398,138 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 28th percentile – i.e., 28% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 465 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 54% of its contemporaries.