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X Demographics
Mendeley readers
| Title |
Generating high-fidelity privacy-conscious synthetic patient data for causal effect estimation with multiple treatments
|
|---|---|
| Published in |
Frontiers in Artificial Intelligence, September 2022
|
| DOI | 10.3389/frai.2022.918813 |
| Pubmed ID | |
| Authors |
Jingpu Shi, Dong Wang, Gino Tesei, Beau Norgeot |
X Demographics
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 2 | 50% |
| Switzerland | 1 | 25% |
| Unknown | 1 | 25% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 4 | 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 | 5 | 23% |
| Researcher | 3 | 14% |
| Student > Master | 2 | 9% |
| Professor > Associate Professor | 1 | 5% |
| Unknown | 11 | 50% |
| Readers by discipline | Count | As % |
|---|---|---|
| Computer Science | 4 | 18% |
| Medicine and Dentistry | 2 | 9% |
| Business, Management and Accounting | 1 | 5% |
| Economics, Econometrics and Finance | 1 | 5% |
| Nursing and Health Professions | 1 | 5% |
| Other | 2 | 9% |
| Unknown | 11 | 50% |