自杀意念
概念化
心理学
单变量
集合(抽象数据类型)
基线(sea)
自杀预防
毒物控制
临床心理学
计算机科学
医学
人工智能
医疗急救
机器学习
多元统计
地质学
海洋学
程序设计语言
作者
Jessica D. Ribeiro,Xieyining Huang,Kathryn R. Fox,Colin G. Walsh,Kathryn P. Linthicum
标识
DOI:10.1177/2167702619838464
摘要
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.
科研通智能强力驱动
Strongly Powered by AbleSci AI