For decades, our ability to predict suicide has remained at near‐chance levels. Machine learning has recently emerged as a promising tool for advancing suicide science, particularly in the domain of suicide prediction. The present review provides an introduction to machine learning and its potential application to open questions in suicide research. Although only a few studies have implemented machine learning for suicide prediction, results to date indicate considerable improvement in accuracy and positive predictive value. Potential barriers to algorithm integration into clinical practice are discussed, as well as attendant ethical issues. Overall, machine learning approaches hold promise for accurate, scalable, and effective suicide risk detection; however, many critical questions and issues remain unexplored.

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.

In recent years, there has been a growing interest in understanding the relationship between sleep and suicide. Although sleep disturbances are commonly cited as critical risk factors for suicidal thoughts and behaviours, it is unclear to what degree sleep disturbances confer risk for suicide. The aim of this meta-analysis was to clarify the extent to which sleep disturbances serve as risk factors (i.e., longitudinal correlates) for suicidal thoughts and behaviours. Our analyses included 156 total effects drawn from 42 studies published between 1982 and 2019. We used a random effects model to analyse the overall effects of sleep disturbances on suicidal ideation, attempts, and death. We additionally explored potential moderators of these associations. Our results indicated that sleep disturbances are statistically significant, yet weak, risk factors for suicidal thoughts and behaviours. The strongest associations were found for insomnia, which significantly predicted suicide ideation (OR 2.10 [95% CI 1.83–2.41]), and nightmares, which significantly predicted suicide attempt (OR 1.81 [95% CI 1.12–2.92]). Given the low base rate of suicidal behaviours, our findings raise questions about the practicality of relying on sleep disturbances as warning signs for imminent suicide risk. Future research is necessary to uncover the causal mechanisms underlying the relationship between sleep disturbances and suicide.

For decades, psychological research has examined the extent to which children's and adolescents' behavior is influenced by the behavior of their peers (i.e., peer influence effects).This review provides a comprehensive synthesis and meta-analysis of this vast field of psychological science, with a goal to quantify the magnitude of peer influence effects across a broad array of behaviors (externalizing, internalizing, academic). To provide a rigorous test of peer influence effects, only studies that employed longitudinal designs, controlled for youths' baseline behaviors, and used "external informants" (peers' own reports or other external reporters) were included. These criteria yielded a total of 233 effect sizes from 60 independent studies across four different continents. A multilevel meta-analytic approach, allowing the inclusion of multiple dependent effect sizes from the same study, was used to estimate an average cross-lagged regression coefficient, indicating the extent to which peers' behavior predicted changes in youths' own behavior over time. Results revealed a peer influence effect that was small in magnitude ( β = 0.08) but significant and robust. Peer influence effects did not vary as a function of the behavioral outcome, age, or peer relationship type (one close friend vs. multiple friends). Time lag and peer context emerged as significant moderators, suggesting stronger peer influence effects over shorter time periods, and when the assessment of peer relationships was not limited to the classroom context. Results provide the most thorough and comprehensive synthesis of childhood and adolescent peer influence to date, indicating that peer influence occurs similarly across a broad range of behaviors and attitudes.

Objective: Efforts to predict nonsuicidal self-injury (NSSI; intentional self-injury enacted without suicidal intent) to date have resulted in near-chance accuracy. Incongruence between theoretical understanding of NSSI and the traditional statistical methods to predict these behaviors may explain this poor prediction. Whereas theoretical models of NSSI assume that the decision to engage in NSSI is relatively complex, statistical models used in NSSI prediction tend to involve simple models with only a few theoretically informed variables. The present study tested whether more complex statistical models would improve NSSI prediction. Method: Within a sample of 1,021 high-risk self-injurious and/or suicidal individuals, we examined the accuracy of three different model types, of increasing complexity, in predicting NSSI across 3, 14, and 28 days. Univariate logistic regressions of each predictor and multiple logistic regression with all predictors were conducted for each timepoint and compared with machine learning algorithms derived from all predictors. Results: Results demonstrated that model complexity was associated with predictive accuracy. Multiple logistic regression models (AUCs 0.70–0.72) outperformed univariate logistic models (average AUCs 0.56). Machine learning models that produced algorithms modeling complex associations across variables produced the strongest NSSI prediction across all time points (AUCs 0.87–0.90). These models outperformed all multiple logistic regression models, including those involving identical study variables. Machine learning algorithm performance remained strong even after the most important factor across algorithms was removed. Conclusions: Results parallel recent findings in suicide research and highlight the complexity that underlies NSSI.