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X Demographics
Mendeley readers
Title |
Machine learning-based prediction of hospital prolonged length of stay admission at emergency department: a Gradient Boosting algorithm analysis
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Published in |
Frontiers in Artificial Intelligence, July 2023
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DOI | 10.3389/frai.2023.1179226 |
Pubmed ID | |
Authors |
Addisu Jember Zeleke, Pierpaolo Palumbo, Paolo Tubertini, Rossella Miglio, Lorenzo Chiari |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
Country | Count | As % |
---|---|---|
Switzerland | 1 | 100% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 22 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 22 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 4 | 18% |
Student > Bachelor | 3 | 14% |
Student > Postgraduate | 2 | 9% |
Lecturer | 1 | 5% |
Other | 1 | 5% |
Other | 3 | 14% |
Unknown | 8 | 36% |
Readers by discipline | Count | As % |
---|---|---|
Engineering | 7 | 32% |
Computer Science | 2 | 9% |
Medicine and Dentistry | 2 | 9% |
Nursing and Health Professions | 1 | 5% |
Economics, Econometrics and Finance | 1 | 5% |
Other | 0 | 0% |
Unknown | 9 | 41% |