Name
Title | ||
|---|---|---|
User-Centered Design of a Machine Learning Intervention for Suicide Risk Prediction in a Military Setting. |
Abstract | ||
|---|---|---|
Primary care represents a major opportunity for suicide prevention in the military. Significant advances have been made in using electronic health record data to predict suicide attempts in patient populations. With a user-centered design approach, we are developing an intervention that uses predictive analytics to inform care teams about their patients\u0027 risk of suicide attempt. We present our experience working with clinicians and staff in a military primary care setting to create preliminary designs and a context-specific usability testing plan for the deployment of the suicide risk indicator. |
Year | Venue | DocType |
|---|---|---|
2020 | AMIA | Conference |
Volume | ISSN | Citations |
2020 | 1942-597X | 0 |
PageRank | References | Authors |
0.34 | 0 | 8 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Carrie Reale | 1 | 0 | 0.34 |
| Laurie L Novak | 2 | 46 | 10.28 |
| Katelyn Robinson | 3 | 0 | 0.34 |
| Christopher L. Simpson | 4 | 3 | 1.57 |
| Jessica D. Ribeiro | 5 | 0 | 0.34 |
| Joseph C. Franklin | 6 | 0 | 0.34 |
| Michael Ripperger | 7 | 0 | 0.68 |
| Colin G Walsh | 8 | 0 | 0.34 |