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X Demographics
Mendeley readers
| Title |
Topic Modeling for Interpretable Text Classification From EHRs
|
|---|---|
| Published in |
Frontiers in Big Data, May 2022
|
| DOI | 10.3389/fdata.2022.846930 |
| Pubmed ID | |
| Authors |
Emil Rijcken, Uzay Kaymak, Floortje Scheepers, Pablo Mosteiro, Kalliopi Zervanou, Marco Spruit |
X Demographics
The data shown below were collected from the profiles of 5 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| France | 1 | 20% |
| Netherlands | 1 | 20% |
| Chile | 1 | 20% |
| United States | 1 | 20% |
| Switzerland | 1 | 20% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 80% |
| Scientists | 1 | 20% |
Mendeley readers
The data shown below were compiled from readership statistics for 27 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 27 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Researcher | 5 | 19% |
| Unspecified | 3 | 11% |
| Student > Master | 3 | 11% |
| Lecturer | 2 | 7% |
| Student > Bachelor | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 13 | 48% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 7 | 26% |
| Unspecified | 3 | 11% |
| Nursing and Health Professions | 1 | 4% |
| Social Sciences | 1 | 4% |
| Medicine and Dentistry | 1 | 4% |
| Other | 0 | 0% |
| Unknown | 14 | 52% |