Identifying long-term and imminent suicide predictors in a general population and a clinical sample with machine learning

Role of machine learning algorithms in suicide risk prediction: a systematic review-meta analysis of clinical studies

OBJECTIVE

Suicide is a complex and multifactorial public health problem. Understanding and addressing the various factors associated with suicide is crucial for prevention and intervention efforts. Machine learning (ML) could enhance the prediction of suicide attempts.

METHOD

A systematic review was performed using PubMed, Scopus, Web of Science and SID databases. We aim to evaluate the performance of ML algorithms and summarize their effects, gather relevant and reliable information to synthesize existing evidence, identify knowledge gaps, and provide a comprehensive list of the suicide risk factors using mixed method approach.

RESULTS

Forty-one studies published between 2011 and 2022, which matched inclusion criteria, were chosen as suitable. We included studies aimed at predicting the suicide risk by machine learning algorithms except natural language processing (NLP) and image processing. The neural network (NN) algorithm exhibited the lowest accuracy at 0.70, whereas the random forest demonstrated the highest accuracy, reaching 0.94. The study assessed the COX and random forest models and observed a minimum area under the curve (AUC) value of 0.54. In contrast, the XGBoost classifier yielded the highest AUC value, reaching 0.97. These specific AUC values emphasize the algorithm-specific performance in capturing the trade-off between sensitivity and specificity for suicide risk prediction. Furthermore, our investigation identified several common suicide risk factors, including age, gender, substance abuse, depression, anxiety, alcohol consumption, marital status, income, education, and occupation. This comprehensive analysis contributes valuable insights into the multifaceted nature of suicide risk, providing a foundation for targeted preventive strategies and intervention efforts.

CONCLUSIONS

The effectiveness of ML algorithms and their application in predicting suicide risk has been controversial. There is a need for more studies on these algorithms in clinical settings, and the related ethical concerns require further clarification.

Childhood sexual abuse and suicide attempts in patients with substance use disorders: The mediating role of emotion dysregulation

BACKGROUND

Suicide attempts (SA) are a public health concern because of increasing prevalence and clinical implications. Childhood trauma (CT) and emotion dysregulation (ED) have been proposed as predictors of SA, but few data are available in patients with Substance Use Disorder (SUD).

OBJECTIVE

Our study aims to investigate the association of sociodemographic/clinical variables, CT typologies, and ED features with SA in SUD patients.

PARTICIPANTS AND SETTING

Subjects with SUD were screened in an outpatient setting. The final sample consisted of 226 patients, subdivided according to the presence of lifetime SA (SUD, n = 163 vs. SUD-SA, n = 63).

METHODS

Participants were compared for sociodemographic and clinical information. CT and ED were assessed through the Childhood Trauma Questionnaire - Short Form (CTQ-SF) and the Difficulties in Emotion Regulation Scale (DERS), respectively. We performed a mediation analysis to test the effect of CT and ED on SA.

RESULTS

Patients with a history of SA (27.9 %) displayed more psychiatric comorbidities (p = 0.002) and hospitalizations (p < 0.001), higher scores at CTQ-SF sexual abuse (p < 0.001) and DERS 'impulse' (p = 0.002), 'goals', 'non-acceptance', 'strategies' (p < 0.001) subscales. The relationship between CTQ-SF sexual abuse and SA was significantly mediated by DERS 'strategies' (p = 0.04; bootstrapped 95 % LLCI-ULCI = 0.004-0.024).

CONCLUSIONS

CT and different dimensions of ED were associated with SA in SUD patients. In our sample, the effects of childhood sexual abuse on SA were mediated by limited access to emotion regulation strategies. SUD patients are burdened with higher all-cause mortality, and CT and lifetime SA can worsen clinical outcomes. Clarifying the reciprocal interactions of psychopathological dimensions may help deliver targeted interventions and reduce suicide risk in specific populations.

Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning

Background

Traditional approaches to modeling suicide-related thoughts and behaviors focus on few data types from often-siloed disciplines. While psychosocial aspects of risk for these phenotypes are frequently studied, there is a lack of research assessing their impact in the context of biological factors, which are important in determining an individual's fulsome risk profile. To directly test this biopsychosocial model of suicide and identify the relative importance of predictive measures when considered together, a transdisciplinary, multivariate approach is needed. Here, we systematically review the emerging literature on large-scale studies using machine learning to integrate measures of psychological, social, and biological factors simultaneously in the study of suicide.

Methods

We conducted a systematic review of studies that used machine learning to model suicide-related outcomes in human populations including at least one predictor from each of biological, psychological, and sociological data domains. Electronic databases MEDLINE, EMBASE, PsychINFO, PubMed, and Web of Science were searched for reports published between August 2013 and August 30, 2023. We evaluated populations studied, features emerging most consistently as risk or resilience factors, methods used, and strength of evidence for or against the biopsychosocial model of suicide.

Results

Out of 518 full-text articles screened, we identified a total of 20 studies meeting our inclusion criteria, including eight studies conducted in general population samples and 12 in clinical populations. Common important features identified included depressive and anxious symptoms, comorbid psychiatric disorders, social behaviors, lifestyle factors such as exercise, alcohol intake, smoking exposure, and marital and vocational status, and biological factors such as hypothalamic-pituitary-thyroid axis activity markers, sleep-related measures, and selected genetic markers. A minority of studies conducted iterative modeling testing each data type for contribution to model performance, instead of reporting basic measures of relative feature importance.

Conclusion

Studies combining biopsychosocial measures to predict suicide-related phenotypes are beginning to proliferate. This literature provides some early empirical evidence for the biopsychosocial model of suicide, though it is marred by harmonization challenges. For future studies, more specific definitions of suicide-related outcomes, inclusion of a greater breadth of biological data, and more diversity in study populations will be needed.