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X Demographics
Mendeley readers
| Title |
Is It Ethical to Use Prognostic Estimates from Machine Learning to Treat Psychosis?
|
|---|---|
| Published in |
The AMA Journal of Ethic, September 2018
|
| DOI | 10.1001/amajethics.2018.804 |
| Pubmed ID | |
| Authors |
Nicole Martinez-Martin, Laura B Dunn, Laura Weiss Roberts |
| Abstract |
Machine learning is a method for predicting clinically relevant variables, such as opportunities for early intervention, potential treatment response, prognosis, and health outcomes. This commentary examines the following ethical questions about machine learning in a case of a patient with new onset psychosis: (1) When is clinical innovation ethically acceptable? (2) How should clinicians communicate with patients about the ethical issues raised by a machine learning predictive model? |
X Demographics
The data shown below were collected from the profiles of 27 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 11 | 41% |
| United Kingdom | 2 | 7% |
| South Africa | 1 | 4% |
| Philippines | 1 | 4% |
| France | 1 | 4% |
| Unknown | 11 | 41% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Members of the public | 17 | 63% |
| Scientists | 7 | 26% |
| Practitioners (doctors, other healthcare professionals) | 2 | 7% |
| Science communicators (journalists, bloggers, editors) | 1 | 4% |
Mendeley readers
The data shown below were compiled from readership statistics for 45 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 45 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Bachelor | 6 | 13% |
| Student > Doctoral Student | 5 | 11% |
| Lecturer | 4 | 9% |
| Other | 4 | 9% |
| Student > Ph. D. Student | 4 | 9% |
| Other | 10 | 22% |
| Unknown | 12 | 27% |
| Readers by discipline | Count | As % |
|---|---|---|
| Medicine and Dentistry | 6 | 13% |
| Psychology | 5 | 11% |
| Computer Science | 5 | 11% |
| Neuroscience | 3 | 7% |
| Social Sciences | 3 | 7% |
| Other | 9 | 20% |
| Unknown | 14 | 31% |