Predicting suicide attempts among soldiers who deny suicidal ideation in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

Bernecker, Samantha L.; Zuromski, Kelly L.; Gutierrez, Peter M.; Joiner, Thomas E.; King, Andrew J.; Liu, Howard; Nock, Matthew K.; Sampson, Nancy A.; Zaslavsky, Alan M.; Stein, Murray B.; Ursano, Robert J.; Kessler, Ronald C.

doi:10.1016/j.brat.2018.11.018

Cited by 35 publications

(37 citation statements)

References 36 publications

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“…In general, exploratory investigations were overrepresented, and replication or application of a predictive model—within a new setting or sample—was rare. Several reports tested replication in a new cohort—within the same setting—or used a multiple-wave sampling approach [ 15 , 26 , 34 , 97 ]. Methodologically, these represent critical areas of importance for future studies and warrant replication.…”

Section: Discussionmentioning

confidence: 99%

“…Regarding generalizability, reports reflected a transdiagnostic focus, and primarily assessed adult participants or patient records. A smaller number of reports examined high-risk, pediatric or geriatric samples, as well as military veterans [ 15 , 26 , 33 , 34 , 53 , 72 , 97 , 112 ]. These highlight areas of elevated need, and align with prioritized strategies and nationally-directed initiatives for technology innovation in suicide prevention [ 5 , 6 , 130 ].…”

Section: Discussionmentioning

confidence: 99%

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Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations

Bernert

Hilberg

Melia

et al. 2020

IJERPH

128

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Suicide is a leading cause of death that defies prediction and challenges prevention efforts worldwide. Artificial intelligence (AI) and machine learning (ML) have emerged as a means of investigating large datasets to enhance risk detection. A systematic review of ML investigations evaluating suicidal behaviors was conducted using PubMed/MEDLINE, PsychInfo, Web-of-Science, and EMBASE, employing search strings and MeSH terms relevant to suicide and AI. Databases were supplemented by hand-search techniques and Google Scholar. Inclusion criteria: (1) journal article, available in English, (2) original investigation, (3) employment of AI/ML, (4) evaluation of a suicide risk outcome. N = 594 records were identified based on abstract search, and 25 hand-searched reports. N = 461 reports remained after duplicates were removed, n = 316 were excluded after abstract screening. Of n = 149 full-text articles assessed for eligibility, n = 87 were included for quantitative synthesis, grouped according to suicide behavior outcome. Reports varied widely in methodology and outcomes. Results suggest high levels of risk classification accuracy (>90%) and Area Under the Curve (AUC) in the prediction of suicidal behaviors. We report key findings and central limitations in the use of AI/ML frameworks to guide additional research, which hold the potential to impact suicide on broad scale.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations

Bernert

Hilberg

Melia

et al. 2020

IJERPH

128

View full text Add to dashboard Cite

show abstract

“…Nonetheless, a risk classification system for soldiers with lifetime SI could be useful given their elevated SA risk as long as a parallel system is developed for soldiers who deny SI. 28 …”

Section: Discussionmentioning

confidence: 99%

Assessment of a Risk Index for Suicide Attempts Among US Army Soldiers With Suicide Ideation

et al. 2019

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Key Points Question Is a short self-report battery associated with improved assessment of risk for suicide attempt among soldiers with suicide ideation? Findings This cohort study of 3649 soldiers participating in the Army Study to Assess Risk and Resilience in Servicemembers survey found that a cross-validated model including self-reported history and severity of suicidal thoughts and behaviors, positive screens for mental disorders, and Army career characteristics was associated with administratively reported suicide attempts 18 to 45 months following baseline among respondents with lifetime suicide ideation at baseline. The 10% of those with suicide ideation who had the highest estimated risk accounted for 39.2% of subsequent suicide attempts. Meaning It may be feasible to develop a clinical risk index for suicide attempt given suicide ideation from a small number of self-report questions.

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“…The authors identified male sex, age of enlistment, criminal offenses, and history of psychiatric illness, suicidality and treatment as the strongest predictors of suicide attempt among soldiers (Kessler et al, 2015). Algorithmic assessment has also demonstrated utility toward identifying suicide risk among soldiers who otherwise deny a history of ideation (Bernecker et al, 2018). Due to the relationship between post-traumatic stress disorder (PTSD) and increased risk of suicide, large datasets like the STARRS are integral to furthering PTSD-related suicide risk prediction.…”

Section: Role Of Ai In Suicide Risk Predictionmentioning

confidence: 99%

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

Fonseka

Bhat

Kennedy

2019

Aust N Z J Psychiatry

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Objective: Suicide is a growing public health concern with a global prevalence of approximately 800,000 deaths per year. The current process of evaluating suicide risk is highly subjective, which can limit the efficacy and accuracy of prediction efforts. Consequently, suicide detection strategies are shifting toward artificial intelligence platforms that can identify patterns within ‘big data’ to generate risk algorithms that can determine the effects of risk (and protective) factors on suicide outcomes, predict suicide outbreaks and identify at-risk individuals or populations. In this review, we summarize the role of artificial intelligence in optimizing suicide risk prediction and behavior management. Methods: This paper provides a general review of the literature. A literature search was conducted in OVID Medline, EMBASE and PsycINFO databases with coverage from January 1990 to June 2019. Results were restricted to peer-reviewed, English-language articles. Conference and dissertation proceedings, case reports, protocol papers and opinion pieces were excluded. Reference lists were also examined for additional articles of relevance. Results: At the individual level, prediction analytics help to identify individuals in crisis to intervene with emotional support, crisis and psychoeducational resources, and alerts for emergency assistance. At the population level, algorithms can identify at-risk groups or suicide hotspots, which help inform resource mobilization, policy reform and advocacy efforts. Artificial intelligence has also been used to support the clinical management of suicide across diagnostics and evaluation, medication management and behavioral therapy delivery. There could be several advantages of incorporating artificial intelligence into suicide care, which includes a time- and resource-effective alternative to clinician-based strategies, adaptability to various settings and demographics, and suitability for use in remote locations with limited access to mental healthcare supports. Conclusion: Based on the observed benefits to date, artificial intelligence has a demonstrated utility within suicide prediction and clinical management efforts and will continue to advance mental healthcare forward.

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Predicting suicide attempts among soldiers who deny suicidal ideation in the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS)

Cited by 35 publications

References 36 publications

Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations

Artificial Intelligence and Suicide Prevention: A Systematic Review of Machine Learning Investigations

Assessment of a Risk Index for Suicide Attempts Among US Army Soldiers With Suicide Ideation

The utility of artificial intelligence in suicide risk prediction and the management of suicidal behaviors

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