| Title |
An interpretable mortality prediction model for COVID-19 patients
|
|---|---|
| Published in |
Nature Machine Intelligence, May 2020
|
| DOI | 10.1038/s42256-020-0180-7 |
| Authors |
Li Yan, Hai-Tao Zhang, Jorge Goncalves, Yang Xiao, Maolin Wang, Yuqi Guo, Chuan Sun, Xiuchuan Tang, Liang Jing, Mingyang Zhang, Xiang Huang, Ying Xiao, Haosen Cao, Yanyan Chen, Tongxin Ren, Fang Wang, Yaru Xiao, Sufang Huang, Xi Tan, Niannian Huang, Bo Jiao, Cheng Cheng, Yong Zhang, Ailin Luo, Laurent Mombaerts, Junyang Jin, Zhiguo Cao, Shusheng Li, Hui Xu, Ye Yuan |
X Demographics
The data shown below were collected from the profiles of 885 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 | 125 | 14% |
| Spain | 64 | 7% |
| Ireland | 38 | 4% |
| United Kingdom | 32 | 4% |
| Japan | 32 | 4% |
| France | 15 | 2% |
| India | 15 | 2% |
| Mexico | 14 | 2% |
| Slovenia | 11 | 1% |
| Other | 123 | 14% |
| Unknown | 416 | 47% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 712 | 80% |
| Scientists | 104 | 12% |
| Practitioners (doctors, other healthcare professionals) | 54 | 6% |
| Science communicators (journalists, bloggers, editors) | 15 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 814 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 814 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 109 | 13% |
| Student > Ph. D. Student | 96 | 12% |
| Student > Master | 90 | 11% |
| Student > Bachelor | 77 | 9% |
| Other | 40 | 5% |
| Other | 148 | 18% |
| Unknown | 254 | 31% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 128 | 16% |
| Computer Science | 123 | 15% |
| Engineering | 58 | 7% |
| Biochemistry, Genetics and Molecular Biology | 51 | 6% |
| Agricultural and Biological Sciences | 23 | 3% |
| Other | 129 | 16% |
| Unknown | 302 | 37% |
Attention Score in Context
This research output has an Altmetric Attention Score of 893. 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 09 April 2022.
All research outputs
#19,963
of 25,775,807 outputs
Outputs from Nature Machine Intelligence
#8
of 778 outputs
Outputs of similar age
#993
of 422,335 outputs
Outputs of similar age from Nature Machine Intelligence
#1
of 31 outputs
Altmetric has tracked 25,775,807 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 99th percentile: it's in the top 5% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 778 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 67.8. This one has done particularly well, scoring higher than 98% 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 422,335 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 99% of its contemporaries.
We're also able to compare this research output to 31 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 96% of its contemporaries.