Predictors Of Suicide Attempts Research Articles

Sex differences in prevalence and clinical correlates of suicide attempts in first-episode and drug-naïve patients with anxious depression in a Chinese Han population: A large-scale cross-sectional study

BackgroundsAnxious depression (AD) has been extensively studied. However, fewer studies have examined sex differences in the prevalence of suicide attempts among AD patients. This study aimed to explore sex differences in suicide attempts and risk factors in patients with AD. Methods1380 first episode drug-naïve patients with AD were recruited. Sociodemographic and clinical characteristics were measured using a self-administered demographic questionnaire. The Hamilton Depression Scale (HAMD), Hamilton Anxiety Inventory (HAMA), and positive subscale of the Positive and Negative Syndrome Scale (PANSS) were used to assess patients' clinical symptoms. We also measured the patient's blood glucose, lipids, and thyroid axis hormone levels. ResultsThere were no sex differences in the prevalence of suicide attempts in patients with FEDN anxious depression. In addition, binary logistic regression analysis showed that HAMA score, TSH levels, and TPOAb levels significantly predicted suicide attempts in both male and female patients with AD, while HAMD score significantly predicted suicide attempts in female patients with AD only. ConclusionsThe severity of anxiety and higher levels of TSH and TPOAb were associated with an increased risk for suicide attempts in both male and female patients with AD, whereas the severity of depression was only associated with suicide attempts in females.

3.79 Predictors of Suicide Attempts Among Adolescents With Suicidal Ideations and Plan

3.79 Predictors of Suicide Attempts Among Adolescents With Suicidal Ideations and Plan

A Risk Calculator to Predict Suicide Attempts Among Individuals With Early-Onset Bipolar Disorder.

To build a one-year risk calculator (RC) to predict individualized risk for suicide attempt in early-onset bipolar disorder. Youth numbering 394 with bipolar disorder who completed ≥2 follow-up assessments (median follow-up length = 13.1 years) in the longitudinal Course and Outcome of Bipolar Youth (COBY) study were included. Suicide attempt over follow-up was assessed via the A-LIFE Self-Injurious/Suicidal Behavior scale. Predictors from the literature on suicidal behavior in bipolar disorder that are readily assessed in clinical practice were selected and trichotomized as appropriate (presence past 6 months/lifetime history only/no lifetime history). The RC was trained via boosted multinomial classification trees; predictions were calibrated via Platt scaling. Half of the sample was used to train, and the other half to independently test the RC. There were 249 suicide attempts among 106 individuals. Ten predictors accounted for >90% of the cross-validated relative influence in the model (AUC = 0.82; in order of relative influence): (1) age of mood disorder onset; (2) non-suicidal self-injurious behavior (trichotomized); (3) current age; (4) psychosis (trichotomized); (5) socioeconomic status; (6) most severe depressive symptoms in past 6 months (trichotomized none/subthreshold/threshold); (7) history of suicide attempt (trichotomized); (8) family history of suicidal behavior; (9) substance use disorder (trichotomized); (10) lifetime history of physical/sexual abuse. For all trichotomized variables, presence in the past 6 months reliably predicted higher risk than lifetime history. This RC holds promise as a clinical and research tool for prospective identification of individualized high-risk periods for suicide attempt in early-onset bipolar disorder.Reprinted from Bipolar Disord 2022; 24:749-757, with permission from John Wiley and Sons. Copyright © 2022.

Deep sequential neural network models improve stratification of suicide attempt risk among US veterans.

To apply deep neural networks (DNNs) to longitudinal EHR data in order to predict suicide attempt risk among veterans. Local explainability techniques were used to provide explanations for each prediction with the goal of ultimately improving outreach and intervention efforts. The DNNs fused demographic information with diagnostic, prescription, and procedure codes. Models were trained and tested on EHR data of approximately 500000 US veterans: all veterans with recorded suicide attempts from April 1, 2005, through January 1, 2016, each paired with 5 veterans of the same age who did not attempt suicide. Shapley Additive Explanation (SHAP) values were calculated to provide explanations of DNN predictions. The DNNs outperformed logistic and linear regression models in predicting suicide attempts. After adjusting for the sampling technique, the convolutional neural network (CNN) model achieved a positive predictive value (PPV) of 0.54 for suicide attempts within 12 months by veterans in the top 0.1% risk tier. Explainability methods identified meaningful subgroups of high-risk veterans as well as key determinants of suicide attempt risk at both the group and individual level. The deep learning methods employed in the present study have the potential to significantly enhance existing suicide risk models for veterans. These methods can also provide important clues to explore the relative value of long-term and short-term intervention strategies. Furthermore, the explainability methods utilized here could also be used to communicate to clinicians the key features which increase specific veterans' risk for attempting suicide.

Predicting suicide attempts among Norwegian adolescents without using suicide-related items: a machine learning approach.

Research on the classification models of suicide attempts has predominantly depended on the collection of sensitive data related to suicide. Gathering this type of information at the population level can be challenging, especially when it pertains to adolescents. We addressed two main objectives: (1) the feasibility of classifying adolescents at high risk of attempting suicide without relying on specific suicide-related survey items such as history of suicide attempts, suicide plan, or suicide ideation, and (2) identifying the most important predictors of suicide attempts among adolescents. Nationwide survey data from 173,664 Norwegian adolescents (ages 13-18) were utilized to train a binary classification model, using 169 questionnaire items. The Extreme Gradient Boosting (XGBoost) algorithm was fine-tuned to classify adolescent suicide attempts, and the most important predictors were identified. XGBoost achieved a sensitivity of 77% with a specificity of 90%, and an AUC of 92.1% and an AUPRC of 47.1%. A coherent set of predictors in the domains of internalizing problems, substance use, interpersonal relationships, and victimization were pinpointed as the most important items related to recent suicide attempts. This study underscores the potential of machine learning for screening adolescent suicide attempts on a population scale without requiring sensitive suicide-related survey items. Future research investigating the etiology of suicidal behavior may direct particular attention to internalizing problems, interpersonal relationships, victimization, and substance use.

Sex-differential association of suicide attempts with thyroid dysfunction in first-episode and drug-naïve young major depressive disorder patients with comorbid anxiety

This study aimed to explore sex differences in the relationship between thyroid function indicators and suicide attempts in first-episode and drug-naïve young major depressive disorder (MDD) patients with comorbid anxiety (MDA). A total of 917 MDD patients (aged 18–35 years) were recruited. The Hamilton depression rating scale (HAMD-17), Hamilton anxiety rating scale (HAMA), positive and negative syndrome scale (PANSS) positive subscale and clinical global impression of severity scale (CGI-S) were used. 467 patients were classified as MDA. The prevalence of suicide attempts was 31.3% in MDA patients, which was significantly higher than that (7.3%) in MDD patients without anxiety. Compared with MDA patients without suicide attempts, MDA patients with suicide attempts were older, had a later age of onset, higher HAMD-17, HAMA, and PANSS positive symptom subscale scores, as well as higher TSH, TgAb and TPOAb levels. For male patients, TSH and TPOAb levels were independently associated with suicide attempts. For female patients, HAMA, PANSS positive symptom scores, CGI-S score and TPOAb levels were independently associated with suicide attempts. Our results suggest that the indicators of thyroid function which can predict suicide attempts in male and female MDA patients have sex differences.

Using iterative random forest to find geospatial environmental and Sociodemographic predictors of suicide attempts.

Despite a recent global decrease in suicide rates, death by suicide has increased in the United States. It is therefore imperative to identify the risk factors associated with suicide attempts to combat this growing epidemic. In this study, we aim to identify potential risk factors of suicide attempt using geospatial features in an Artificial intelligence framework. We use iterative Random Forest, an explainable artificial intelligence method, to predict suicide attempts using data from the Million Veteran Program. This cohort incorporated 405,540 patients with 391,409 controls and 14,131 attempts. Our predictive model incorporates multiple climatic features at ZIP-code-level geospatial resolution. We additionally consider demographic features from the American Community Survey as well as the number of firearms and alcohol vendors per 10,000 people to assess the contributions of proximal environment, access to means, and restraint decrease to suicide attempts. In total 1,784 features were included in the predictive model. Our results show that geographic areas with higher concentrations of married males living with spouses are predictive of lower rates of suicide attempts, whereas geographic areas where males are more likely to live alone and to rent housing are predictive of higher rates of suicide attempts. We also identified climatic features that were associated with suicide attempt risk by age group. Additionally, we observed that firearms and alcohol vendors were associated with increased risk for suicide attempts irrespective of the age group examined, but that their effects were small in comparison to the top features. Taken together, our findings highlight the importance of social determinants and environmental factors in understanding suicide risk among veterans.

The amplitude of low-frequency fluctuation characteristics in depressed adolescents with suicide attempts: a resting-state fMRI study.

The amplitude of low-frequency fluctuation (ALFF) is a measure of spontaneous brain activity derived from resting-state functional magnetic resonance imaging (rs-fMRI). Previous research has suggested that abnormal ALFF values may be associated with major depressive disorder (MDD) and suicide attempts in adolescents. In this study, our aim was to investigate the differences in ALFF values between adolescent MDD patients with and without a history of suicide attempts, and to explore the potential utility of ALFF as a neuroimaging biomarker for aiding in the diagnosis and prediction of suicide attempts in this population. The study included 34 adolescent depression patients with suicide attempts (SU group), 43 depression patients without suicide attempts (NSU group), and 36 healthy controls (HC group). Depression was diagnosed using a threshold score greater than 17 on the Hamilton Depression Rating Scale (HDRS). The rs-fMRI was employed to calculate zALFF values and compare differences among the groups. Associations between zALFF values in specific brain regions and clinical variables such as emotion regulation difficulties were explored using Pearson partial correlation analysis. Receiver-Operating Characteristics (ROC) analysis assessed the ability of mean zALFF values to differentiate between SU and NSU groups. Significant differences in zALFF values were observed in the left and right inferior temporal gyrus (l-ITG, r-ITG) and right fusiform gyrus (r-FG) among the three groups (GRF corrected). Both SU and NSU groups exhibited increased zALFF values in the inferior temporal gyrus compared to the HC group. Furthermore, the SU group showed significantly higher zALFF values in the l-ITG and r-FG compared to both the NSU group and the HC group. Partial correlation analysis revealed a negative correlation between zALFF values in the left superior and middle frontal gyrus (l-SFG, l-MFG) and the degree of emotional dysregulation in the SU group (R = -0.496, p = 0.003; R = -0.484, p = 0.005). Combining zALFF values from the l-ITG and r-FG achieved successful discrimination between depressed adolescents with and without suicide attempts (AUC = 0.855) with high sensitivity (86%) and specificity (71%). Depressed adolescents with suicidal behavior exhibit unique neural activity patterns in the inferior temporal gyrus and fusiform gyrus. These findings highlight the potential utility of these specific brain regions as biomarkers for identifying suicide risk in depressed adolescents. Furthermore, associations between emotion dysregulation and activity in their frontal gyrus regions were observed. These findings provide preliminary yet pertinent insights into the pathophysiology of suicide in depressed adolescents.

Predicting suicidal events: A comparison of the Concise Health Risk Tracking Self-Report (CHRT-SR) and the Columbia Suicide Severity Rating Scale (C-SSRS)

This report examines the predictive capabilities of two scales of suicidality in high-risk adolescents. Charts of adolescents with severe suicidality participating in an intensive outpatient program were reviewed. Self-report data from the 9-item Concise Health Risk Tracking Self-Report (CHRT-SR9) and clinician-completed data from the Columbia Suicide Severity Risk Scale (C-SSRS) were obtained at entry. Scales’ performances in predicting suicide attempts and suicidal events were evaluated using logistic regression models and ROC analyses. Of 539 adolescents, 53 had events of which 19 were attempts. The CHRT-SR9 total score predicted events (OR=1.05) and attempts (OR=1.09), as did the C-SSRS Suicide Ideation (SI) Intensity Composite for events (OR=1.10) and attempts (OR=1.16). The CHRT-SR9 AUC was 0.70 (84.2% sensitivity; 41.7% specificity; PPV=5.0%; NPV=98.6%) for attempts. The C-SSRS Intensity Composite AUC was 0.62 (89.5% sensitivity; 24.1% specificity; PPV=4.2%; NPV=98.4%) for attempts. Both the CHRT-SR9 and C-SSRS capture important parameters related to suicidal events or attempts that can help assess suicidal risk in adolescents.

Predictors of suicide attempt within 30 days of first medically documented major depression diagnosis in U.S. army soldiers with no prior suicidal ideation

BackgroundUnderstanding mental health predictors of imminent suicide attempt (SA; within 30 days) among soldiers with depression and no prior suicide ideation (SI) can inform prevention and treatment. The current study aimed to identify sociodemographic and service-related characteristics and mental disorder predictors associated with imminent SA among U.S. Army soldiers following first documented major depression diagnosis (MDD) with no history of SI.MethodsIn this case-control study using Army Study to Assess Risk and Resilience in Servicemembers (STARRS) administrative data, we identified 101,046 active-duty Regular Army enlisted soldiers (2010–2016) with medically-documented MDD and no prior SI (MDD/No-SI). We examined risk factors for SA within 30 days of first MDD/No-SI using logistic regression analyses, including socio-demographic/service-related characteristics and psychiatric diagnoses.ResultsThe 101,046 soldiers with documented MDD/No-SI were primarily male (78.0%), < 29 years old (63.9%), White (58.1%), high school-educated (74.5%), currently married (62.0%) and < 21 when first entering the Army (56.9%). Among soldiers with MDD/No-SI, 2,600 (2.6%) subsequently attempted suicide, 16.2% (n = 421) within 30 days (rate: 416.6/100,000). Our final multivariable model identified: Soldiers with less than high school education (χ23 = 11.21, OR = 1.5[95%CI = 1.2–1.9]); combat medics (χ22 = 8.95, OR = 1.5[95%CI = 1.1–2.2]); bipolar disorder (OR = 3.1[95%CI = 1.5–6.3]), traumatic stress (i.e., acute reaction to stress/not PTSD; OR = 2.6[95%CI = 1.4–4.8]), and “other” diagnosis (e.g., unspecified mental disorder: OR = 5.5[95%CI = 3.8-8.0]) diagnosed same day as MDD; and those with alcohol use disorder (OR = 1.4[95%CI = 1.0-1.8]) and somatoform/dissociative disorders (OR = 1.7[95%CI = 1.0-2.8]) diagnosed before MDD were more likely to attempt suicide within 30 days. Currently married soldiers (χ22 = 6.68, OR = 0.7[95%CI = 0.6–0.9]), those in service 10 + years (χ23 = 10.06, OR = 0.4[95%CI = 0.2–0.7]), and a sleep disorder diagnosed same day as MDD (OR = 0.3[95%CI = 0.1–0.9]) were less likely.ConclusionsSA risk within 30 days following first MDD is more likely among soldiers with less education, combat medics, and bipolar disorder, traumatic stress, and “other” disorder the same day as MDD, and alcohol use disorder and somatoform/dissociative disorders before MDD. These factors identify imminent SA risk and can be indicators for early intervention.

Persistent Non-Suicidal Self-Injury and Suicidality in Referred Adolescents: A Longitudinal Study Exploring the Role of Cyclothymic Temperament.

Non-suicidal self-injury (NSSI) is deliberate harm to the body surface without suicidal intent, though it may be a predictor of suicide attempts. Our aim was to test the hypothesis that persisting and recovering NSSI may have a different longitudinal risk for suicidal ideation and behavior and that the intensity of Cyclothymic Hypersensitive Temperament (CHT) may increase this risk. Fifty-five patients (mean age 14.64 ± 1.77 years) referred for mood disorders according to the DSM-5 were consecutively recruited and followed-up for a mean of 19.79 ± 11.67 months and grouped according to the presence/absence of NSSI at baseline and follow-up into three groups: without NSSI (non-NSSI; n = 22), with NSSI recovered at follow-up (past-NSSI; n = 19), and with persistent NSSI at follow-up (pers-NSSI; n = 14). At follow-up, both NSSI groups were more severely impaired and failed to improve internalizing problems and dysregulation symptoms. Both NSSI groups reported higher scores in suicidal ideation compared to non-NSSI, but only pers-NSSI presented higher scores in suicidal behavior. CHT was higher in pers-NSSI, followed by past-NSSI and then by non-NSSI. Our data support a continuity between NSSI and suicidality, and they suggest the prognostic validity of persistent NSSI, associated with highest CHT scores.

A Suicide-Specific Diagnosis – The Case For

A Suicide-Specific Diagnosis – The Case For

Predicting Lifetime Suicide Attempts in a Community Sample of Adolescents Using Machine Learning Algorithms.

Suicide is a major global health concern and a prominent cause of death in adolescents. Previous research on suicide prediction has mainly focused on clinical or adult samples. To prevent suicides at an early stage, however, it is important to screen for risk factors in a community sample of adolescents. We compared the accuracy of logistic regressions, elastic net regressions, and gradient boosting machines in predicting suicide attempts by 17-year-olds in the Millennium Cohort Study (N = 7,347), combining a large set of self- and other-reported variables from different categories. Both machine learning algorithms outperformed logistic regressions and achieved similar balanced accuracies (.76 when using data 3 years before the self-reported lifetime suicide attempts and .85 when using data from the same measurement wave). We identified essential variables that should be considered when screening for suicidal behavior. Finally, we discuss the usefulness of complex machine learning models in suicide prediction.

Suicide attempts, suicide and their association with socio-demographic variables in Iran: A retrospective, registry-based, cohort study (2016–2021)

INTRODUCTION: Suicide is recognized as one of the most significant concerns in healthcare and a serious psychological health issue in many countries throughout the world. Suicide attempts occur in all social sectors and demographics. It is important to know what are the causes for people to try to commit suicide for an effective prevention and control. The aim of this review was to find out the levels and predictors of suicide attempts. MATERIAL AND METHODS: A cross-sectional descriptive-analytical research looked for suicide attempt reported cases at Dr. Moaven Hospital in Sahneh, Iran, during the 2016–2021 period. Data was collected via checklists completed by supervisors during referrals and subsequently analyzed using the SPSS Statistics software tool (version 24). Descriptive statistics and the Chi-square test were used. A significance threshold of 0.05 was used. RESULTS: A total of 1,059 cases of suicide attempts were found. The highest prevalence rate was reported in the group ages 16–25. Males had higher rates of cases (57.4%), which were twice more prevalent in cities and more common in lower-socioeconomic-status families Furthermore, the most common method used (79.5%) was medication intake. There was also a significant association between marital status, job, and suicide-attempt rates (p &lt; 0.05). CONCLUSIONS: Suicide attempts are among the most important issues in terms of psychosocial healthcare in all countries and communities, and their prevalence rates may be determined by a variety of factors, such as mental health status, family and living conditions, financial problems and unemployment rate. Preventive control of these factors can contribute to reducing the prevalence of these acts.

Predicting suicide attempts among people with diabetes using a large multicenter electronic health records dataset.

People with diabetes have a higher risk of suicidal behaviors than the general population. However, few studies have focused on understanding this relationship. We investigated risk factors and predicted suicide attempts in people with diabetes using Least Absolute Shrinkage and Selection Operator (LASSO) regression. Data was retrieved from Cerner Real-World Data and included over 3 million diabetes patients in the study. LASSO regression was applied to identify associated factors. Gender, diabetes-type, and depression-specific LASSO regression models were analyzed. There were 7764 subjects diagnosed with suicide attempts with an average age of 45. Risk factors for suicide attempts in diabetes patients were American Indian or Alaska Native race (β = 0.637), receiving atypical antipsychotic agents (β = 0.704), benzodiazepines (β = 0.784), or antihistamines (β = 0.528). Amyotrophy was negatively associated with suicide attempts in males (β = 2.025); in contrast, amyotrophy significantly increased the risk in females (β = 3.339). Using a MAOI was negatively related to suicide attempts in T1DM patients (β = 7.304). Age less than 20 was positively associated with suicide attempts in depressed (β = 2.093) and non-depressed patients (β = 1.497). The LASSO model achieved a 94.4% AUC and 87.4% F1 score. To our knowledge, this is the first study to use LASSO regression to identify risk factors for suicide attempts in patients with diabetes. The shrinkage technique successfully reduced the number of variables in the model to improve the fit. Further research is needed to determine cause-and-effect relationships. The results may help providers to identify high-risk groups for suicide attempt among diabetic patients.

Predicting Suicide Attempts among Major Depressive Disorder Patients with Structural Neuroimaging: A Machine Learning Approach

IntroductionEvery year at least one million people die by suicide, with major depressive disorder (MDD) being one of the major causes of suicide deaths. Current suicide risk assessments rely on subjective information, are time consuming, low predictive, and poorly reliable. Thus, finding objective biomarkers of suicidality is crucial to move clinical practice towards a precision psychiatry framework, enhancing suicide risk detection and prevention for MDD.ObjectivesThe present study aimed at applying machine learning (ML) algorithms on both grey matter and white-matter voxel-wise data to discriminate MDD suicide attempters (SA) from non-attempters (nSA).Methods91 currently depressed MDD patients (24 SA, 67 nSA) underwent a structural MRI session. T1-weighted images and diffusion tensor imaging scans were respectively pre-processed using Computational Atlas Toolbox 12 (CAT12) and spatial tract-based statistics (TBSS) on FSL, to obtain both voxel-based morphometry (VBM) and fractional anisotropy (FA) measures. Three classification models were built, entering whole-brain VBM and FA maps alone into a Support Vector Machine (SVM) and combining both modalities into a Multiple Kernel Learning (MKL) algorithm. All models were trained through a 5-fold nested cross-validation with subsampling to calculate reliable estimates of balanced accuracy, specificity, sensitivity, and area under the receiver operator curve (AUC).ResultsModels’ performances are summarized in Table 1.Table 1.Models’ performances.Input featuresAlgorithmSpecificitySensitivityBalanced accuracyAUCVBMSVM55.00%50.00%52.50%0.55FASVM72.00%54.00%63.00%0.62VBM and FAMKL68.00%54.00%61.00%0.58Abbreviations: AUC, area under the receiver operator curve; FA, fractional anisotropy; VBM, voxel-based morphometry.ConclusionsOverall, although overcoming the random classification accuracy (i.e., 50%), performances of all models classifying SA and nSA MDD patients were moderate, possibly due to the imbalanced numerosity of classes, with SVM on FA reaching the highest accuracy. Thus, future studies may enlarge the sample and add different features (e.g., functional neuroimaging data) to develop an objective and reliable predictive model to assess and hence prevent suicide risk among MDD patients.Disclosure of InterestNone Declared

Prediction of suicidality in bipolar disorder using variability of intrinsic brain activity and machine learning.

Bipolar disorder (BD) is associated with marked suicidal susceptibility, particularly during a major depressive episode. However, the evaluation of suicidal risk remains challenging since it relies mainly on self-reported information from patients. Hence, it is necessary to complement neuroimaging features with advanced machine learning techniques in order to predict suicidal behavior in BD patients. In this study, a total of 288 participants, including 75 BD suicide attempters, 101 BD nonattempters and 112 healthy controls, underwent aresting-state functional magnetic resonance imaging (rs-fMRI). Intrinsic brain activity was measured by amplitude of low-frequency fluctuation (ALFF). We trained and tested a two-level k-nearest neighbors (k-NN) model based on resting-state variability of ALFF with fivefold cross-validation. BD suicide attempters had increased dynamic ALFF values in the right anterior cingulate cortex, left thalamus and right precuneus. Compared to other machine learning methods, our proposed framework had a promising performance with 83.52% accuracy, 78.75% sensitivity and 87.50% specificity. The trained models could also replicate and validate the results in an independent cohort with 72.72% accuracy. These findings based on a relatively large data set, provide a promising way of combining fMRI data with machine learning technique to reliably predict suicide attempt at an individual level in bipolar depression. Overall, this work might enhance our understanding of the neurobiology of suicidal behavior by detecting clinically defined disruptions in the dynamics of instinct brain activity.

Suicide risk model based on the interpersonal theory of suicide: evidence in three regions of Mexico

Background: Reports of suicidal behavior have increased in Mexico for years. In order to develop a more adequate suicide prevention strategy, it is necessary to understand its predictive factors, so the purpose of this research was to propose a model of suicidal risk in young people, taking into account one of the most current theories on the subject, Joiner's interpersonal theory. Method: A non-probabilistic sample of young people with suicidal ideation from three regions of Mexico was obtained by online survey (N=411), with mean age of 17.89 years (SD. 1.2), 336 women (81.8%), and 75 men (18.2%). Results: First, a multiple linear regression model was created to predict suicidal risk based on thwarted belongingness and perceived burden with 17% explained variance; then a second model was generated with the same variables and including other factors associated with suicide such as self-injury desires, impulsivity and suicide attempts, in addition to variables associated with family conflicts, improving the explained variance to 34%. Lastly, two properly adjusted structural equation models were obtained, one focused on suicidal risk (R2=.21; RMSEA=.026; CFI=.99) and the other on ideation (R2=.18; RMSEA=.070; CFI=.98). Conclusions: The main factors that explain suicidal risk are depressive symptoms, perceived burden and desires for self-injury. Further research on the effect of painful experiences as factors that could predict suicide attempt is suggested.

Using Behavioral Measures to Assess Suicide Risk in the Psychiatric Emergency Department for Youth.

Suicide screening is critical in pediatric emergency departments (EDs). Behavioral measures of suicide risk may complement self-report measures. The current study examines suicide-specific behavioral measures and tests their potential short-term within-person effects among respondents, ability to discriminate future suicide attempt from suicidal ideation, and translation into interpretable categorical composite scores. The sample included 167 youth (10-17years), presenting for suicide-related reasons to a pediatric psychiatric ED. During their ED visit, participants completed the Death/Life Implicit Association Test (IAT) and the Suicide Stroop Task. Recurrent suicidal thoughts and attempts were assessed within 6months of the ED visit via medical records and email surveys. Youth displayed a decrease in the levels of distress and self-injurious desires (negative mood, desire to hurt themselves, and desire to die) after completing the behavioral tasks. The Death/Life IAT prospectively differentiated with 68% accuracy between youth who attempted suicide after their ED visit and those who had suicidal ideation but no attempt, p = 0.04, OR = 5.65, although this effect became marginally significant after controlling for self-report and demographic covariates. Neither the Suicide Stroop Task, nor the categorical composite scores predicted suicide attempts, ps = 0.08-0.87, ORs = 0.96-3.95. Behavioral measures of suicide risk administered in the ED do not appear to increase distress or self-injurious desires. They may be able to distinguish those who go on to attempt suicide (vs. consider suicide) within six months after discharge.

Review on Machine Learning Based Hybrid Model for Suicide Attempt Prediction

Suicide attempts is a critical issue in modern society of the world. In this paper early detection and prediction of suicide attempts should be proposed to save people’s life. Machine learning techniques with feature engineering or deep learning for automatic detection based on online social contents and to perform awareness are currently suicidal information of detection methods. Clinical methods based on interaction between social workers or experts and the targeted individuals. The first survey that provides a comprehensive overview of these categories' methods and performance is included in this paper. The data sources of process applications of suicide attempts and detection—such as questionnaires, electronic health records, suicide notes, and online user content and datasets—are examined. In order to make it easier to continue the analysis of the research, numerous specific tasks and datasets are described and summarized. Finally, we offer a perspective on the proposed work's future research directions and a summary of the work's limitations. Key Words: Suicidal ideation detection, social content, features engineering, Machine learning.