Citation

Ribeiro JD, Huang X, Fox KR, Walsh CG, Linthicum KP. Clinical Psychological Science 2019; 7(5): 941-957.

Copyright

(Copyright © 2019, Association for Psychological Science, Publisher SAGE Publishing)

DOI

10.1177/2167702619838464

PMID

unavailable

Abstract

For decades, our ability to predict suicidal thoughts and behaviors (STBs) has been at near-chance levels. The objective of this study was to advance prediction by addressing two major methodological constraints pervasive in past research: (a) the reliance on long follow-ups and (b) the application of simple conceptualizations of risk. Participants were 1,021 high-risk suicidal and/or self-injuring individuals recruited worldwide. Assessments occurred at baseline and 3, 14, and 28 days after baseline using a range of implicit and self-report measures. Retention was high across all time points (> 90%). Risk algorithms were derived and compared with univariate analyses at each follow-up.

RESULTS indicated that short-term prediction alone did not improve prediction for attempts, even using commonly cited "warning signs"; however, a small set of factors did provide fair-to-good short-term prediction of ideation. Machine learning produced considerable improvements for both outcomes across follow-ups.

RESULTS underscore the importance of complexity in the conceptualization of STBs. © The Author(s) 2019.

Language: en

Keywords

suicide; suicidal behavior; prediction; imminent risk; machine learning