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X Demographics
Mendeley readers
| Title |
Comparing different machine learning techniques for predicting COVID-19 severity
|
|---|---|
| Published in |
Infectious Diseases of Poverty, February 2022
|
| DOI | 10.1186/s40249-022-00946-4 |
| Pubmed ID | |
| Authors |
Yibai Xiong, Yan Ma, Lianguo Ruan, Dan Li, Cheng Lu, Luqi Huang |
X Demographics
The data shown below were collected from the profiles of 10 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 | 3 | 30% |
| India | 2 | 20% |
| France | 1 | 10% |
| Spain | 1 | 10% |
| United Kingdom | 1 | 10% |
| Unknown | 2 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 9 | 90% |
| Science communicators (journalists, bloggers, editors) | 1 | 10% |
Mendeley readers
The data shown below were compiled from readership statistics for 57 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 57 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 6 | 11% |
| Student > Bachelor | 5 | 9% |
| Student > Master | 3 | 5% |
| Lecturer | 3 | 5% |
| Researcher | 3 | 5% |
| Other | 9 | 16% |
| Unknown | 28 | 49% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 13 | 23% |
| Medicine and Dentistry | 6 | 11% |
| Biochemistry, Genetics and Molecular Biology | 3 | 5% |
| Engineering | 2 | 4% |
| Environmental Science | 1 | 2% |
| Other | 4 | 7% |
| Unknown | 28 | 49% |