| Title |
Prediction of Disease Progression of COVID-19 Based upon Machine Learning
|
|---|---|
| Published in |
International Journal of General Medicine, April 2021
|
| DOI | 10.2147/ijgm.s294872 |
| Pubmed ID | |
| Authors |
Fumin Xu, Xiao Chen, Xinru Yin, Qiu Qiu, Jingjing Xiao, Liang Qiao, Mi He, Liang Tang, Xiawei Li, Qiao Zhang, Yanling Lv, Shili Xiao, Rong Zhao, Yan Guo, Mingsheng Chen, Dongfeng Chen, Liangzhi Wen, Bin Wang, Yongjian Nian, Kaijun Liu |
X Demographics
The data shown below were collected from the profile of 1 X user 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 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 19 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 19 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 3 | 16% |
| Student > Doctoral Student | 2 | 11% |
| Student > Master | 2 | 11% |
| Student > Bachelor | 1 | 5% |
| Lecturer > Senior Lecturer | 1 | 5% |
| Other | 3 | 16% |
| Unknown | 7 | 37% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 5 | 26% |
| Nursing and Health Professions | 3 | 16% |
| Computer Science | 2 | 11% |
| Immunology and Microbiology | 1 | 5% |
| Agricultural and Biological Sciences | 1 | 5% |
| Other | 0 | 0% |
| Unknown | 7 | 37% |
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 April 2021.
All research outputs
#18,807,229
of 23,308,124 outputs
Outputs from International Journal of General Medicine
#975
of 1,490 outputs
Outputs of similar age
#321,597
of 431,714 outputs
Outputs of similar age from International Journal of General Medicine
#50
of 78 outputs
Altmetric has tracked 23,308,124 research outputs across all sources so far. This one is in the 11th percentile – i.e., 11% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,490 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 9.1. This one is in the 21st percentile – i.e., 21% of its peers scored the same or lower than it.
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 431,714 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 15th percentile – i.e., 15% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 78 others from the same source and published within six weeks on either side of this one. This one is in the 21st percentile – i.e., 21% of its contemporaries scored the same or lower than it.