| Title |
Machine learning-based CT radiomics method for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection: a multicenter study
|
|---|---|
| Published in |
Annals of Translational Medicine, July 2020
|
| DOI | 10.21037/atm-20-3026 |
| Pubmed ID | |
| Authors |
Hongmei Yue, Qian Yu, Chuan Liu, Yifei Huang, Zicheng Jiang, Chuxiao Shao, Hongguang Zhang, Baoyi Ma, Yuancheng Wang, Guanghang Xie, Haijun Zhang, Xiaoguo Li, Ning Kang, Xiangpan Meng, Shan Huang, Dan Xu, Junqiang Lei, Huihong Huang, Jie Yang, Jiansong Ji, Hongqiu Pan, Shengqiang Zou, Shenghong Ju, Xiaolong Qi |
X Demographics
The data shown below were collected from the profiles of 2 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Netherlands | 1 | 50% |
| Unknown | 1 | 50% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 1 | 50% |
| Practitioners (doctors, other healthcare professionals) | 1 | 50% |
Mendeley readers
The data shown below were compiled from readership statistics for 219 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 219 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 29 | 13% |
| Researcher | 27 | 12% |
| Student > Master | 26 | 12% |
| Student > Bachelor | 19 | 9% |
| Other | 14 | 6% |
| Other | 39 | 18% |
| Unknown | 65 | 30% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 56 | 26% |
| Computer Science | 25 | 11% |
| Engineering | 16 | 7% |
| Biochemistry, Genetics and Molecular Biology | 6 | 3% |
| Nursing and Health Professions | 6 | 3% |
| Other | 31 | 14% |
| Unknown | 79 | 36% |
Attention Score in Context
This research output has an Altmetric Attention Score of 10. 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 17 December 2020.
All research outputs
#3,235,012
of 23,230,825 outputs
Outputs from Annals of Translational Medicine
#223
of 2,919 outputs
Outputs of similar age
#84,082
of 398,624 outputs
Outputs of similar age from Annals of Translational Medicine
#11
of 133 outputs
Altmetric has tracked 23,230,825 research outputs across all sources so far. Compared to these this one has done well and is in the 86th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 2,919 research outputs from this source. They receive a mean Attention Score of 4.6. This one has done particularly well, scoring higher than 92% 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 398,624 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 78% of its contemporaries.
We're also able to compare this research output to 133 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 91% of its contemporaries.