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X Demographics
Mendeley readers
Title |
Predicting the Disease Outcome in COVID-19 Positive Patients Through Machine Learning: A Retrospective Cohort Study With Brazilian Data
|
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Published in |
Frontiers in Artificial Intelligence, August 2021
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DOI | 10.3389/frai.2021.579931 |
Pubmed ID | |
Authors |
Fernanda Sumika Hojo De Souza, Natália Satchiko Hojo-Souza, Edimilson Batista Dos Santos, Cristiano Maciel Da Silva, Daniel Ludovico Guidoni |
X Demographics
The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Switzerland | 1 | 25% |
Egypt | 1 | 25% |
Unknown | 2 | 50% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 4 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 100 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 100 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Master | 11 | 11% |
Student > Bachelor | 11 | 11% |
Student > Ph. D. Student | 9 | 9% |
Researcher | 8 | 8% |
Other | 3 | 3% |
Other | 13 | 13% |
Unknown | 45 | 45% |
Readers by discipline | Count | As % |
---|---|---|
Medicine and Dentistry | 18 | 18% |
Computer Science | 12 | 12% |
Engineering | 4 | 4% |
Agricultural and Biological Sciences | 3 | 3% |
Biochemistry, Genetics and Molecular Biology | 2 | 2% |
Other | 11 | 11% |
Unknown | 50 | 50% |