The data shown below were collected from the profiles of 9 X users who shared this research output. Click here to find out more about how the information was compiled.
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X Demographics
Mendeley readers
| Title |
Symptom Prediction and Mortality Risk Calculation for COVID-19 Using Machine Learning
|
|---|---|
| Published in |
Frontiers in Artificial Intelligence, June 2021
|
| DOI | 10.3389/frai.2021.673527 |
| Pubmed ID | |
| Authors |
Elham Jamshidi, Amirhossein Asgary, Nader Tavakoli, Alireza Zali, Farzaneh Dastan, Amir Daaee, Mohammadtaghi Badakhshan, Hadi Esmaily, Seyed Hamid Jamaldini, Saeid Safari, Ehsan Bastanhagh, Ali Maher, Amirhesam Babajani, Maryam Mehrazi, Mohammad Ali Sendani Kashi, Masoud Jamshidi, Mohammad Hassan Sendani, Sahand Jamal Rahi, Nahal Mansouri |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 1 | 11% |
| Switzerland | 1 | 11% |
| United Kingdom | 1 | 11% |
| Unknown | 6 | 67% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 8 | 89% |
| Scientists | 1 | 11% |
Mendeley readers
The data shown below were compiled from readership statistics for 83 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 83 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 9 | 11% |
| Student > Bachelor | 9 | 11% |
| Researcher | 4 | 5% |
| Student > Doctoral Student | 4 | 5% |
| Lecturer | 3 | 4% |
| Other | 14 | 17% |
| Unknown | 40 | 48% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 16 | 19% |
| Computer Science | 4 | 5% |
| Nursing and Health Professions | 4 | 5% |
| Engineering | 3 | 4% |
| Biochemistry, Genetics and Molecular Biology | 2 | 2% |
| Other | 9 | 11% |
| Unknown | 45 | 54% |