Austin Goddard
Biographic Data
| ID | 7957352 |
|---|---|
| NAME | Austin Goddard |
| GIVEN NAMES | Austin |
| FAMILY NAME | Goddard |
| SIGNATURE | GODDARD A |
| AFFILIATIONS | University of Utah |
| VERIFIED | No |
| TOTAL WORKS | 2 |
| TOTAL CITATIONS | 0 |
| WORKS AS AUTHOR | 2 |
| WORKS AS EDITOR | 0 |
| FIRST PUBLICATION YEAR | 2022 |
| LATEST PUBLICATION YEAR | 2024 |
| H-INDEX | 0 |
Multi-environment prediction of suicidal beliefs
Suicide disproportionately affects the military and veteran population, yet the task of identifying those at an increased risk of suicidal behavior remains challenging. In the face of this complex issue, novel machine learning methods have been applied to study the relationship between suicide and potential risk factors, but are often not generalizable to new and unseen samples. Herein, we examine the problem of prediction on unknown environments…
Invariance-based causal prediction to identify the direct causes of suicidal behavior
Despite decades of research, the direct causes of suicide remain unknown. Some researchers have proposed that suicide is sufficiently complex that no single variable or set of variables can be determined causal. The invariance-based causal prediction (ICP) is a contemporary data analytic method developed to identify the direct causal relationships, but the method has not yet been applied to suicide. In this study, we used ICP to identify the vari…
No prominent works on this page.
Invariance-based causal prediction to identify the direct causes of suicidal behavior
Despite decades of research, the direct causes of suicide remain unknown. Some researchers have proposed that suicide is sufficiently complex that no single variable or set of variables can be determined causal. The invariance-based causal prediction (ICP) is a contemporary data analytic method developed to identify the direct causal relationships, but the method has not yet been applied to suicide. In this study, we used ICP to identify the vari…
Multi-environment prediction of suicidal beliefs
Suicide disproportionately affects the military and veteran population, yet the task of identifying those at an increased risk of suicidal behavior remains challenging. In the face of this complex issue, novel machine learning methods have been applied to study the relationship between suicide and potential risk factors, but are often not generalizable to new and unseen samples. Herein, we examine the problem of prediction on unknown environments…
Clinical Psychology (2 works) · Clinical Psychology (2 works) · Medical emergency (2 works) · Medicine (2 works) · Poison control (2 works) · Psychology (2 works) · Sample (material (2 works) · Suicide and Self-Harm Studies (2 works) · Suicide prevention (2 works) · Causal model (1 work)