Predictors Of Suicide Risk Research Articles

Artificial intelligence in suicide prevention: Utilizing deep learning approach for early detection

ABSTRACT Background: Suicide among students is increasing in India and is a matter of grave concern. Early identification of students contemplating suicide would facilitate emergency intervention and may save precious lives. Aim: Our primary objective was to construct an artificial intelligence (AI) model employing an artificial neural network (ANN) architecture to predict students at risk of suicidal tendencies. This initiative was prompted by the necessity to implement a proactive and technologically driven strategy for identifying competitive exam-bound students facing heightened vulnerability. The aim was to facilitate timely interventions aimed at reducing the risk of self-harm. Materials and Methods: An AI model utilizing ANNs is devised for suicide risk prediction among exam-stressed students. A 33-feature input layer is curated based on literature and expert insights, with binary features assigned weighted values. A rigorous hyperparameter optimization approach using the Optuna library to select the most effective neural network model. Ridge regression was used to determine bias or variance in the dataset. Training and testing of the model are conducted using fictional and simulated profiles, respectively, and model performance is assessed through statistical metrics and the Cohen’s Kappa coefficient, benchmarked against expert evaluations. Result: The AI model demonstrates exceptional predictive capabilities for suicide risk assessment among competitive exam students. Quantitative Metrics: The model’s accuracy of 98% aligns predictions with outcomes, distinguishing risk categories. Precision at 100% identifies cases within predicted risks, minimizing false positives. A recall of 97% identifies true risk cases, highlighting sensitivity. F1 Score: The model’s F1 score of 98% balances precision and recall, indicating overall performance. Cohen’s Kappa: With a coefficient of 1.00, the model’s substantial agreement with experts underscores its consistent classifications. Conclusion: The study introduces an AI model utilizing ANNs for suicide risk prediction among stressed students. High precision, recall, and accuracy align with expert evaluations, highlighting its promise for timely risk identification. The model’s efficiency in evaluating large populations swiftly indicates its clinical potential. Refinement and real-world validation remain future considerations.

Prediction of Suicidal Thoughts and Suicide Attempts in People Who Gamble Based on Biological-Psychological-Social Variables: A Machine Learning Study.

Recent research has shown that people who gamble are more likely to have suicidal thoughts and attempts compared to the general population. Despite the advancements made, no study to date has predicted suicide risk factors in people who gamble using machine learning algorithms. Therefore, current study aimed to identify the most critical predictors of suicidal ideation and suicidal attempts among people who gamble using a machine learning approach. An online survey conducted a cross-sectional analysis of 741 people who gamble (mean age: 25.9 ± 5.56). To predict the risk of suicide attempts and ideation, we employed a comprehensive set of 40 biological, psychological, social, and socio-demographic variables. The predictive models were developed using Logistic Regression, Random Forest (RF), robust eXtreme Gradient Boosting (XGBoost), and ensemble machine learning algorithms. Data analysis was performed using R-Studio software. Random Forest emerged as the top-performing algorithm for predicting suicidal ideation, with an impressive AUC of 0.934, sensitivity of 0.7514, specificity of 0.9885, PPV of 0.9473, and NPV of 0.9347. Across all models, dissociation, depression, and anxiety symptoms consistently emerged as crucial predictors of suicidal ideation. However, for suicide attempt prediction, all models exhibited weaker performance. XGBoost showed the best performance in this regard, with an AUC of 0.663, sensitivity of 0.78, specificity of 0.8990, PPV of 0.34, NPV of 0.984, and accuracy of 0.8918. Depressive symptoms and rumination severity were highlighted as the most important predictors of suicide attempts according to this model. These findings have important implications for clinical practice and public health interventions. Machine learning could help detect individuals prone to suicidal ideation and suicide attempts among people who gamble, assisting in creating tailored prevention programs to address future suicide risks more effectively.

Acceptability of linking individual credit, financial, and public records data to healthcare records for suicide risk machine learning models.

Individual-level information about negative life events (NLE) such as bankruptcy, foreclosure, divorce, and criminal arrest might improve the accuracy of machine learning models for suicide risk prediction. Individual-level NLE data is routinely collected by vendors such as Equifax. However, little is known about the acceptability of linking this NLE data to healthcare data. Our objective was to assess preferences for linking external NLE data to healthcare records for suicide prevention. We conducted a discrete choice experiment (DCE) among Kaiser Permanente Washington (KPWA) members. Patient partners assisted in the design and pretesting of the DCE survey. The DCE included 12 choice tasks involving 4 data linking program attributes and 3 levels within each attribute. We estimated latent class conditional logit models to derive preference weights. There were 743 participants. Willingness to link data varied by type of information to be linked, demographic characteristics, and experience with NLE. Overall, 65.1% of people were willing to link data and 34.9% were more private. Trust in KPWA to safeguard data was the strongest predictor of willingness to link data. Most respondents supported linking NLE data for suicide prevention. Contrary to expectations, People of Color and people who reported experience with NLEs were more likely to be willing to link their data. A majority of participants were willing to have their credit and public records data linked to healthcare records provided that conditions are in place to protect privacy and autonomy.

A Nomogram for Predicting Suicide Risk and Prognostic Factors in Bladder Cancer Patients Following Diagnosis: More Information Needed.

A Nomogram for Predicting Suicide Risk and Prognostic Factors in Bladder Cancer Patients Following Diagnosis: More Information Needed.

Predicting suicidal ideation from irregular and incomplete time series of questionnaires in a smartphone-based suicide prevention platform: a pilot study

Over 700,000 people die by suicide annually. Collecting longitudinal fine-grained data about at-risk individuals, as they occur in the real world, can enhance our understanding of the temporal dynamics of suicide risk, leading to better identification of those in need of immediate intervention. Self-assessment questionnaires were collected over time from 89 at-risk individuals using the EMMA smartphone application. An artificial intelligence (AI) model was trained to assess current level of suicidal ideation (SI), an early indicator of the suicide risk, and to predict its progression in the following days. A key challenge was the unevenly spaced and incomplete nature of the time series data. To address this, the AI was built on a missing value imputation algorithm. The AI successfully distinguished high SI levels from low SI levels both on the current day (AUC = 0.804, F1 = 0.625, MCC = 0.459) and three days in advance (AUC = 0.769, F1 = 0.576, MCC = 0.386). Besides past SI levels, the most significant questions were related to psychological pain, well-being, agitation, emotional tension, and protective factors such as contacts with relatives and leisure activities. This represents a promising step towards early AI-based suicide risk prediction using a smartphone application.

Adaptation and validation of a suicide-focused Word Sentence Association Paradigm to assess suicide-specific interpretation biases

The cognitive model of suicide proposes that biased cognitive processes contribute to suicidal thoughts and behaviors, and initial evidence suggests that attentional bias to suicide-congruent cues may predict suicidal behavior beyond established clinical risk factors. To date, much less work has explored the potential role of interpretation biases in the development and prediction of suicide risk. The present study assessed the psychometric properties, including reliability, concurrent validity, and predictive validity, of a novel adaptation of the Word Sentence Association Paradigm (WSAP) to assess suicide-suicide interpretation biases. In a sample of 239 psychiatric inpatients, including patients with no recent suicidal ideation or lifetime attempts (n = 35), those with recent suicide attempts (n = 29), and those with recent suicidal ideation (n = 174), participants completed the modified WSAP as well as self-reported suicidal ideation and attempts for the subsequent 6 months. Although the WSAP demonstrated good reliability, evidence of concurrent and prospective validity (in terms of self-reported suicidal ideation and attempts) was limited. Specifically, the clinical groups did not differ from one another on any measure of interpretation bias, nor did suicide-specific interpretation endorsements predict concurrent or future suicidal ideation when controlling for dysphoric interpretation bias. However, suicide-specific interpretation biases were uniquely associated with a history of a lifetime suicide attempt. Future work should further clarify the extent and specificity of the relationship between suicide-specific interpretation biases and clinical outcomes.

Age-stratified predictions of suicide attempts using machine learning in middle and late adolescence

BackgroundPrevalence of suicidal behaviour increases rapidly in middle to late adolescence. Predicting suicide attempts across different ages would enhance our understanding of how suicidal behaviour manifests in this period of rapid development. This study aimed to develop separate models to predict suicide attempts within a cohort at middle and late adolescence. It also sought to examine differences between the models derived across both developmental stages. MethodsThis study used data from the nationally representative Longitudinal Study of Australian Children (N = 2266). We selected over 700 potential suicide attempt predictors measured via self-report questionnaires, and linked healthcare and education administrative datasets. Logistic regression, random forests, and gradient boosting algorithms were developed to predict suicide attempts across two stages (mid-adolescence: 14–15 years; late adolescence: 18–19 years) using predictors sampled two years prior (mid-adolescence: 12–13 years; late adolescence: 16–17 years). ResultsThe late adolescence models (AUROC = 0.77–0.88, F1-score = 0.22–0.28, Sensitivity = 0.54–0.64) performed better than the mid-adolescence models (AUROC = 0.70–0.76, F1-score = 0.12–0.19, Sensitivity = 0.40–0.64). The most important features for predicting suicide attempts in mid-adolescence were mostly school-related, while the most important features in late adolescence included measures of prior suicidality, psychosocial health, and future plans. ConclusionsTo date, this is the first study to use machine learning models to predict suicide attempts at different ages. Our findings suggest that the optimal suicide risk prediction model differs by stage of adolescence. Future research and interventions should consider that risk presentations can change rapidly during adolescence.

Individual suicide risk factors with resting-state brain functional connectivity patterns in bipolar disorder patients based on latent Dirichlet allocation model

BackgroundThe widespread problem of suicide and its severe burden in bipolar disorder (BD) necessitate the development of objective risk markers, aiming to enhance individual suicide risk prediction in BD. MethodsThis study recruited 123 BD patients (61 patients with prior suicide attempted history (PSAs), 62 without (NSAs)) and 68 healthy controls (HEs). The Latent Dirichlet Allocation (LDA) model was used to decompose the resting state functional connectivity (RSFC) into multiple hyper/hypo-RSFC patterns. Thereafter, according to the quantitative results of individual heterogeneity over latent factor dimensions, the correlations were analyzed to test prediction ability. ResultsModel constructed without introducing suicide-related labels yielded three latent factors with dissociable hyper/hypo-RSFC patterns. In the subsequent analysis, significant differences in the factor distributions of PSAs and NSAs showed biases on the default-mode network (DMN) hyper-RSFC factor (factor 3) and the salience network (SN) and central executive network (CEN) hyper-RSFC factor (factor 1), indicating predictive value. Correlation analysis of the individuals' expressions with their Nurses' Global Assessment of Suicide Risk (NGASR) revealed factor 3 positively correlated (r = 0.4180, p < 0.0001) and factor 1 negatively correlated (r = − 0.2492, p = 0.0055) with suicide risk. Therefore, it could be speculated that patterns more associated with suicide reflected hyper-connectivity in DMN and hypo-connectivity in SN, CEN. ConclusionsThis study provided individual suicide-associated risk factors that could reflect the abnormal RSFC patterns, and explored the suicide related brain mechanisms, which is expected to provide supports for clinical decision-making and timely screening and intervention for individuals at high risks of suicide.

Suicide among Veterans in Veterans Health Administration care: Differences in methods by tier of predicted suicide risk

Veterans' suicide rates exceed those of non-Veteran adults, and Veteran suicides more often involve firearms. However, the relationship between Veteran characteristics and suicide method is not well understood. Using the Veteran Health Administration suicide prediction algorithm, we assessed differences in suicide method by predicted risk among Veteran suicide decedents. Significant predictors of firearm involvement included lower algorithm-predicted suicide risk, no recent suicide attempts, male sex, and older age. Non-firearm methods were most common at high tiers of suicide risk. Firearm safety counseling is important for all Veterans and non-firearm lethal means safety counseling may be particularly important for high-risk Veterans.

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

ObjectiveSuicide 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.MethodA 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.ResultsForty-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.ConclusionsThe 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.

Nutrition facts, drug facts, and model facts: putting AI ethics into practice in gun violence research.

Firearm injury research necessitates using data from often-exploited vulnerable populations of Black and Brown Americans. In order to reduce bias against protected attributes, this study provides a theoretical framework for establishing trust and transparency in the use of AI with the general population. We propose a Model Facts template that is easily extendable and decomposes accuracy and demographics into standardized and minimally complex values. This framework allows general users to assess the validity and biases of a model without diving into technical model documentation. We apply the Model Facts template on 2 previously published models, a violence risk identification model and a suicide risk prediction model. We demonstrate the ease of accessing the appropriate information when the data are structured appropriately. The Model Facts template is limited in its current form to human based data and biases. Like nutrition facts, it will require educational programs for users to grasp its full utility. Human computer interaction experiments should be conducted to ensure model information is communicated accurately and in a manner that improves user decisions. The Model Facts label is the first framework dedicated to establishing trust with end users and general population consumers. Implementation of Model Facts into firearm injury research will provide public health practitioners and those impacted by firearm injury greater faith in the tools the research provides.

The effect of bullying and cyberbullying on predicting suicide risk in adolescent females: The mediating role of depression

This paper analyzed the role of depression as a mediator in the association between bullying, cyberbullying, and suicide risk in adolescent females. A total of 751 Colombian adolescent females (M= 13.71, SD=1.897), who were administered the Plutchik Suicide Risk Scale, the Beck Depression Inventory, the European Bullying Intervention Project Questionnaire and Cyberbullying. Bullying victimization and aggression and cyberbullying victimization were found to contribute statistically significant effects that explaining 22 % of the variance in depression. The variables of victimization in bullying and cyberbullying and depression explained 64 % of the variance in suicidal risk, and depression mediated the association between victim and aggressor roles in bullying and cyberbullying in predicting suicidal risk, whose total direct and indirect effects are statistically significant. The findings support the role of depression as a mediating variable between bullying and cyberbullying and suicidal risk in female adolescents and highlight the importance of focusing prevention and intervention efforts on risk factors for depression and suicidal behavior in cases of bullying and cyberbullying.

Assessment of Suicide Risk in Patients with Depressive Episodes Due to Affective Disorders and Borderline Personality Disorder: A Pilot Comparative Study.

This study assessed suicidal risk in patients suffering from non-psychotic depressive disorders within various clinical and nosological forms (F31-F34 mood disorders and F60.31-emotionally unstable personality disorder). Clinical and psychological features were presented, as well as predictors of suicidal risk in patients of these groups. We performed a comparative analysis of the anxiety and depression level, the level of mental pain, fear of death and the severity of anti-suicidal motives in patients with affective disorders and borderline personality disorder (BPD). Based on the results, 100% of patients in these clinical nosological groups were found to have a high level of suicidal risk. Patients with affective disorders have weak anti-suicidal motives and are not fully aware of the consequences of their own death. Patients with BPD have a higher suicidal risk than patients with affective disorders; they are characterized by less pronounced social orientation, demonstrativeness, self-centeredness, less pronounced levels of anxiety and fear of death.

Revealing suicide risk of young adults based on comprehensive measurements using decision tree classification

Predicting suicide risk based on risk and protective factors is a critical and complex endeavor. In this study, we combined insights from comprehensive aetiological theories on suicide with the methodological strengths of machine learning techniques. Our primary objectives were twofold: a) to identify hazardous feature combinations that characterize a high risk of suicide, and b) to enhance our understanding of the potential interactions between risk and protective factors related to suicide. We established an interpretable decision tree model to classify young adults at high risk of suicide, utilizing fifty-five variables covering distal, developmental, proximal, and social context factors from a large-scale cross-sectional survey (N = 88,214). The results highlighted the significance of variables such as self-compassion and non-suicidal self-injury (NSSI), and the accumulation of depressive symptoms, medium-to-low self-compassion, and a history of NSSI as substantial indicators of heightened suicide risk. This study serves as a valuable reference for the clinical identification of individuals at risk of suicide.

Artificial intelligence (AI) in efforts to prevent teenage suicide: literature review

Suicide is a highly complex mental health issue that is a leading cause of death and requires efforts to reduce the number of victims. In the modern technology era, the utilization of artificial intelligence (AI) is seen as a suicide prevention initiative and presents a significant challenge in global prevention efforts. This literature review aims to determine the use of Artificial Intelligence (AI) in efforts to prevent teenage suicide. The research method utilizes the PRISMA guidelines. This literature review employs a systematic approach and selection process. Literature sources were searched from Proquest, PubMed, Google Scholar, and Scopus databases. Out of the 7 reviewed articles, 2 were from South Korea, 2 from the United States, and the remaining 3 were from Spain, Italy, and Canada. The Cohort research design was the most prevalent in this literature review (N = 5), and one study used an RCT design (N = 1), while a Cross-Sectional research design was employed in one study (N = 1). Overall, it indicates that AI is capable of predicting suicide risk and preventing suicide. The results of the literature review indicate that the use of AI technology has benefits in preventing teenage suicide.

Analysis and evaluation of explainable artificial intelligence on suicide risk assessment

This study explores the effectiveness of Explainable Artificial Intelligence (XAI) for predicting suicide risk from medical tabular data. Given the common challenge of limited datasets in health-related Machine Learning (ML) applications, we use data augmentation in tandem with ML to enhance the identification of individuals at high risk of suicide. We use SHapley Additive exPlanations (SHAP) for XAI and traditional correlation analysis to rank feature importance, pinpointing primary factors influencing suicide risk and preventive measures. Experimental results show the Random Forest (RF) model is excelling in accuracy, F1 score, and AUC (>97% across metrics). According to SHAP, anger issues, depression, and social isolation emerge as top predictors of suicide risk, while individuals with high incomes, esteemed professions, and higher education present the lowest risk. Our findings underscore the effectiveness of ML and XAI in suicide risk assessment, offering valuable insights for psychiatrists and facilitating informed clinical decisions.

Enhancing Suicide Risk Prediction with Polygenic Scores: A Prospective Study in Psychiatric Emergency Settings (Preprint)

Enhancing Suicide Risk Prediction with Polygenic Scores: A Prospective Study in Psychiatric Emergency Settings (Preprint)

Прогноз суицидального риска у сотрудников силовых ведомств, в том числе военнослужащих

Suicide is a major medical and social concern for law enforcement, a contemporary army included, not only in Russia but abroad as well. In recent years, frequency of suicides and suicidal attempts has been growing among law enforcement officials (LEOs), military personnel included. Therefore, it seems relevant to develop a model for predicting suicide risk. In this study, our aim was to develop a model for predicting suicide risk in LEOs based on express testing results. Our research object was represented by LEOs (n = 591), their average age being 23.71 ± 1.12 years. To assess suicide risk, we used a questionnaire for suicide risk assessment ‘SSR-2’, which is a part of DAP-2 methodology for deviant behavior research, and a clinical-psychopathological method. LEOs’ personality characteristics and their current psychophysiological state were identified by using vibration imaging, a technology for recording and mathematically analyzing micro-vibrations of the head and face. It has certain advantages over its analogues. We determined psychophysiological characteristics, basic abilities (types of Gardner’s multiple intelligence) and moral qualities that differed in people with elevated suicide risk against the control. We identified a difference between unconscious reactions of the examined people to stimuli and declared (conscious) ones, which indicates that LEOs tend to hide any signs of suicidal behavior in them. A mathematical model was built for predicting suicide risk: we developed an integral suicide risk assessment and created a probabilistic nomogram that makes it possible to establish likelihood of suicidal behavior signs with accuracy above 98 % relying on results obtained by a 5-minute express test. Use of this predictive model helps identify those people among personnel who should undergo a profound check-up by a psychological support team. Our research results can serve as a basis for creating an objective concept for diagnostics of suicide risk factors in LEOs.

Prognosis of suicidal risk among law enforcement officials including military personnel

Suicide is a major medical and social concern for law enforcement, a contemporary army included, not only in Russia but abroad as well. In recent years, frequency of suicides and suicidal attempts has been growing among law enforcement officials (LEOs), military personnel included. Therefore, it seems relevant to develop a model for predicting suicide risk. In this study, our aim was to develop a model for predicting suicide risk in LEOs based on express testing results. Our research object was represented by LEOs (n = 591), their average age being 23.71 ± 1.12 years. To assess suicide risk, we used a questionnaire for suicide risk assessment ‘SSR-2’, which is a part of DAP-2 methodology for deviant behavior research, and a clinical-psychopathological method. LEOs’ personality characteristics and their current psychophysiological state were identified by using vibration imaging, a technology for recording and mathematically analyzing micro-vibrations of the head and face. It has certain advantages over its analogues. We determined psychophysiological characteristics, basic abilities (types of Gardner’s multiple intelligence) and moral qualities that differed in people with elevated suicide risk against the control. We identified a difference between unconscious reactions of the examined people to stimuli and declared (conscious) ones, which indicates that LEOs tend to hide any signs of suicidal behavior in them. A mathematical model was built for predicting suicide risk: we developed an integral suicide risk assessment and created a probabilistic nomogram that makes it possible to establish likelihood of suicidal behavior signs with accuracy above 98 % relying on results obtained by a 5-minute express test. Use of this predictive model helps identify those people among personnel who should undergo a profound check-up by a psychological support team. Our research results can serve as a basis for creating an objective concept for diagnostics of suicide risk factors in LEOs.

Suicide Risk Screening for Head and Neck Cancer Patients: An Implementation Study.

There is limited research on suicide risk screening (SRS) among head and neck cancer (HNC) patients, a population at increased risk for suicide. To address this gap, this single-site mixed methods study assessed oncology professionals' perspectives about the feasibility, acceptability, and appropriateness of an electronic SRS program that was implemented as a part of routine care for HNC patients. Staff who assisted with SRS implementation completed (e.g., nurses, medical assistants, advanced practice providers, physicians, social workers) a one-time survey (N = 29) and interview (N = 25). Quantitative outcomes were assessed using previously validated feasibility, acceptability, and appropriateness measures. Additional qualitative data were collected to provide context for interpreting the scores. Nurses and medical assistants, who were directly responsible for implementing SRS, reported low feasibility, acceptability, and appropriateness, compared with other team members (e.g., physicians, social workers, advanced practice providers). Team members identified potential improvements needed to optimize SRS, such as hiring additional staff, improving staff training, providing different modalities for screening completion among individuals with disabilities, and revising the patient-reported outcomes to improve suicide risk prediction. Staff perspectives about implementing SRS as a part of routine cancer care for HNC patients varied widely. Before screening can be implemented on a larger scale for HNC and other cancer patients, additional implementation strategies may be needed that optimize workflow and reduce staff burden, such as staff training, multiple modalities for completion, and refined tools for identifying which patients are at greatest risk for suicide.