Validation of the Computerized Suicide Risk Scale--a backpropagation neural network instrument (CSRS-BP)

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

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.

BP neural network prediction model for suicide attempt among Chinese rural residents

OBJECTIVE

This study aimed to establish and assess the Back Propagation Neural Network (BPNN) prediction model for suicide attempt, so as to improve the individual prediction accuracy.

METHOD

Data was collected from a wide range case-control suicide attempt survey. 659 serious suicide attempters (case group) were randomly recruited through the hospital emergency and patient registration system from 13 rural counties in China. Each case was matched the control by same community, gender, and similar age (± 2 ages). Face to face interviews were conducted for each subject with structured questionnaire. Logistic regression was applied to preliminarily screen the factors and BPNN was used to establish the prediction model of suicide attempt.

RESULTS

Multivariate logistic regression indicated that family history of suicide (OR = 4.146), mental problem (OR = 3.876) Low education level, poor health, aspiration strain, hopelessness, impulsivity, depression are the risk predictors and social support, coping skills, healthy community are the protect predictors for suicide attempt. Repetitious data simulation process of BPNN indicated that three-layer BPNN with 9 hidden layer neurons is the optimal prediction model. The sensitivity (67.6%), specificity (93.9%), positive predictive value (86.0%), negative predictive value (84.1%), total coincidence rate (84.6%) manifested that it is excellent to distinguish suicide attempt case.

CONCLUSIONS

The BPNN method is applicative, feasible, credible and good discriminative effect for suicide attempt. The BPNN established has significant clinical meaning for the clinical psychiatrist and lay theoretical foundation for artificial intelligence expert assisted diagnosis system for suicide attempt in the future.

Suicide risk factors and suicide vulnerability in various major psychiatric disorders

PRIMARY OBJECTIVES

There are many known suicide risk factors (SRF) common to major psychiatric disorders, but their impact on suicide vulnerability remains unclear. We used FALCON (Fuzzy Adaptive Learning Control Network) to evaluate those impacts.

METHODS

Staff psychiatrists completed computerized suicide risk scales (CSRS-III) including 21 SRF for 612 patients. Diagnoses were: schizophrenia, schizoaffective, major depression, anxiety disorder, bipolar affective disorder, personality disorder, organic brain syndromes, delusional disorder and other diagnoses. An optimal trained FALCON was obtained by running the network 10 times with 552 CSRS-III, validating with the balance. Medically serious suicide attempts (the vulnerability factor) served as the target variable. The significance of each variable in the trained network was determined by the magnitude of the change in output as affected by the consecutive change in all points of the variable input, then calculating the mean variance of all cases. The direction of influence was determined by the input on the entire scale of each variable, point by point, across all cases, then calculating the mean of all outputs.

RESULTS

The impact and direction of influence of the various SRF differed for each diagnosis.

CONCLUSION

Evaluation of the individual patient with his/her specific impact profile, determination of direction of influence of the corresponding SRF's may assist in increasing the accuracy of individual suicide risk assessment.

Suicide and its prevention among older adults

OBJECTIVE

To review the research on the epidemiology, risk and resiliency, assessment, treatment, and prevention of late-life suicide.

METHOD

I reviewed mortality statistics. I searched MEDLINE and PsycINFO databases for research on suicide risk and resiliency and for randomized controlled trials with suicidal outcomes. I also reviewed mental health outreach and suicide prevention initiatives.

RESULTS

Approximately 12/100,000 individuals aged 65 years or over die by suicide in Canada annually. Suicide is most prevalent among older white men; risk is associated with suicidal ideation or behaviour, mental illness, personality vulnerability, medical illness, losses and poor social supports, functional impairment, and low resiliency. Novel measures to assess late-life suicide features are under development. Few randomized treatment trials exist with at-risk older adults.

CONCLUSIONS

Research is needed on risk and resiliency and clinical assessment and interventions for at-risk older adults. Collaborative outreach strategies might aid suicide prevention.