| Title |
Diabetes emergency cases identification based on a statistical predictive model
|
|---|---|
| Published in |
Journal of Big Data, March 2022
|
| DOI | 10.1186/s40537-022-00582-7 |
| Authors |
Kebira Azbeg, Mohcine Boudhane, Ouail Ouchetto, Said Jai Andaloussi |
Mendeley readers
The data shown below were compiled from readership statistics for 78 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 78 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 5 | 6% |
| Student > Master | 5 | 6% |
| Student > Postgraduate | 4 | 5% |
| Student > Doctoral Student | 3 | 4% |
| Lecturer | 3 | 4% |
| Other | 11 | 14% |
| Unknown | 47 | 60% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 14 | 18% |
| Engineering | 5 | 6% |
| Business, Management and Accounting | 2 | 3% |
| Medicine and Dentistry | 2 | 3% |
| Psychology | 2 | 3% |
| Other | 5 | 6% |
| Unknown | 48 | 62% |