Improving the accuracy of suicide attempter classification

Investigating suicidal behavior among prisoners in the light of the behavioral addiction approach: results of a multicenter cross-sectional study

Introduction

The behavioral addiction model posits that repetitive suicidal behaviors can serve as maladaptive strategies for managing stress and negative emotional states, akin to substance addiction. Both behaviors involve negative emotions, offer temporary psychological relief, and persist, indicating shared neurobiological mechanisms. This study explored psychometric differences among major repeaters, occasional attempters, and non-suicidal prisoners.

Methods

A multi-centre cross-sectional survey of 363 inmates across four prisons assessed depression, cognitive-emotional regulation, impulsivity, perceived stress, lifetime non-suicidal self-injury and suicide attempts.

Results

Mild depression, moderate suicidal ideation, and moderate impulsivity were common, with nearly half of the participants having attempted suicide at least once. Hierarchical multiple regression analyses revealed that repeated suicidal behavior in the past increases susceptibility to future suicidal thoughts, with suicide attempts serving as a maladaptive emotion regulation strategy among repeated attempters.

Discussion

The results reveal differences in emotional dysregulation, impulsivity, and stress coping strategies among the studied groups, reinforcing the idea of suicidality as a form of behavioral addiction. The addiction approach helps explain the sensitivity to later suicidal thoughts in former attempters and self-harmers, offering valuable insights for tailored interventions within correctional settings.

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.

Machine learning and the prediction of suicide in psychiatric populations: a systematic review

Machine learning (ML) has emerged as a promising tool to enhance suicidal prediction. However, as many large-sample studies mixed psychiatric and non-psychiatric populations, a formal psychiatric diagnosis emerged as a strong predictor of suicidal risk, overshadowing more subtle risk factors specific to distinct populations. To overcome this limitation, we conducted a systematic review of ML studies evaluating suicidal behaviors exclusively in psychiatric clinical populations. A systematic literature search was performed from inception through November 17, 2022 on PubMed, EMBASE, and Scopus following the PRISMA guidelines. Original research using ML techniques to assess the risk of suicide or predict suicide attempts in the psychiatric population were included. An assessment for bias risk was performed using the transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD) guidelines. About 1032 studies were retrieved, and 81 satisfied the inclusion criteria and were included for qualitative synthesis. Clinical and demographic features were the most frequently employed and random forest, support vector machine, and convolutional neural network performed better in terms of accuracy than other algorithms when directly compared. Despite heterogeneity in procedures, most studies reported an accuracy of 70% or greater based on features such as previous attempts, severity of the disorder, and pharmacological treatments. Although the evidence reported is promising, ML algorithms for suicidal prediction still present limitations, including the lack of neurobiological and imaging data and the lack of external validation samples. Overcoming these issues may lead to the development of models to adopt in clinical practice. Further research is warranted to boost a field that holds the potential to critically impact suicide mortality.

Forecasting Unplanned Purchase Behavior under Buy-One Get-One-Free Promotions Using Functional Near-Infrared Spectroscopy

It is very important for consumers to recognize their wrong shopping habits such as unplanned purchase behavior (UPB). The traditional methods used for measuring the UPB in qualitative and quantitative studies have some drawbacks because of human perception and memory. We proposed a UPB identification methodology applied with the brain-computer interface technique using a support vector machine (SVM) along with a functional near-infrared spectroscopy (fNIRS). Hemodynamic signals and behavioral data were collected from 33 subjects by performing Task 1 which included the Buy-One-Get-One-Free (BOGOF) and Task 2 which excluded the BOGOF condition. The acquired data were calculated with 6 time-domain features and then classified them using SVM with 10-cross validations. Thereafter, we evaluated whether the results were reliable using the area under the receiver operating characteristic curve (AUC). As a result, we achieved average accuracy greater than 94%, which is reliable because of the AUC values above 0.97. We found that the UPB brain activity was more relevant to Task 1 with the BOGOF condition than with Task 2 in the prefrontal cortex. UPBs were sufficiently derived from self-reported measurement, indicating that the subjects perceived increased impulsivity in the BOGOF condition. Therefore, this study improves the detection and understanding of UPB as a path for a computer-aided detection perspective for rating the severity of UPBs.

Prospective prediction of suicide attempts in community adolescents and young adults, using regression methods and machine learning

BACKGROUND

The use of machine learning (ML) algorithms to study suicidality has recently been recommended. Our aim was to explore whether ML approaches have the potential to improve the prediction of suicide attempt (SA) risk. Using the epidemiological multiwave prospective-longitudinal Early Developmental Stages of Psychopathology (EDSP) data set, we compared four algorithms-logistic regression, lasso, ridge, and random forest-in predicting a future SA in a community sample of adolescents and young adults.

METHODS

The EDSP Study prospectively assessed, over the course of 10 years, adolescents and young adults aged 14-24 years at baseline. Of 3021 subjects, 2797 were eligible for prospective analyses because they participated in at least one of the three follow-up assessments. Sixteen baseline predictors, all selected a priori from the literature, were used to predict follow-up SAs. Model performance was assessed using repeated nested 10-fold cross-validation. As the main measure of predictive performance we used the area under the curve (AUC).

RESULTS

The mean AUCs of the four predictive models, logistic regression, lasso, ridge, and random forest, were 0.828, 0.826, 0.829, and 0.824, respectively.

CONCLUSIONS

Based on our comparison, each algorithm performed equally well in distinguishing between a future SA case and a non-SA case in community adolescents and young adults. When choosing an algorithm, different considerations, however, such as ease of implementation, might in some instances lead to one algorithm being prioritized over another. Further research and replication studies are required in this regard.

Cognitive, Emotional, Temperament, and Personality Trait Correlates of Suicidal Behavior

Suicide is one of the leading causes of violent death in many countries and its prevention is included in worldwide health objectives. Currently, the DSM-5 considers suicidal behavior as an entity that requires further study. Among the three validators required for considering a psychiatric disorder, there is one based on psychological correlates, biological markers, and patterns of comorbidity. This review includes the most important and recent studies on psychological factors: cognitive, emotional, temperament, and personality correlates (unrelated to diagnostic criteria). We included classic factors related to suicidal behavior such as cognitive, inflexibility, problem-solving, coping, rumination, thought suppression, decision-making, autobiographical memory, working memory, language fluency, burdensomeness, belongingness, fearless, pain insensitivity, impulsiveness, aggressiveness, and hopelessness. The personality correlates reported are mainly based on the personality theories of Cloninger, Costa and McCrae, and Eysenck. Moreover, it explores conceptual links to other new pathways in psychological factors, emptiness, and psychological pain as a possible origin and common end path for a portion of suicidal behaviors.

Characterization of suicidal behaviour with self-organizing maps

The study of the variables involved in suicidal behavior is important from a social, medical, and economical point of view. Given the high number of potential variables of interest, a large population of subjects must be analysed in order to get conclusive results. In this paper, we describe a method based on self-organizing maps (SOMs) for finding the most relevant variables even when their relation to suicidal behavior is strongly nonlinear. We have applied the method to a cohort with more than 8,000 subjects and 600 variables and discovered four groups of variables involved in suicidal behavior. According to the results, there are four main groups of risk factors that characterize the population of suicide attempters: mental disorders, alcoholism, impulsivity, and childhood abuse. The identification of specific subpopulations of suicide attempters is consistent with current medical knowledge and may provide a new avenue of research to improve the management of suicidal cases.

Combining scales to assess suicide risk

OBJECTIVES

A major interest in the assessment of suicide risk is to develop an accurate instrument, which could be easily adopted by clinicians. This article aims at identifying the most discriminative items from a collection of scales usually employed in the assessment of suicidal behavior.

METHODS

The answers to the Barrat Impulsiveness Scale, International Personality Disorder Evaluation Screening Questionnaire, Brown-Goodwin Lifetime History of Aggression, and Holmes & Rahe Social Readjustment Rating Scale provided by a group of 687 subjects (249 suicide attempters, 81 non-suicidal psychiatric inpatients, and 357 healthy controls) were used by the Lars-en algorithm to select the most discriminative items.

RESULTS

We achieved an average accuracy of 86.4%, a specificity of 89.6%, and a sensitivity of 80.8% in classifying suicide attempters using 27 out of the 154 items from the original scales.

CONCLUSIONS

The 27 items reported here should be considered a preliminary step in the development of a new scale evaluating suicidal risk in settings where time is scarce.

Genetic epistasis in female suicide attempters

BACKGROUND

Complex behaviors such as suicidal behavior likely exhibit gene-gene interactions. The main aim of this study is to explore potential single nucleotide polymorphisms combinations with epistatic effect in suicidal behavior using a data mining tool (Multifactor Dimensionality Reduction).

METHODS

Genomic DNA from peripheral blood samples was analyzed using SNPlex Technology. Multifactor Dimensionality Reduction was used to detect epistatic interactions between single nucleotide polymorphisms from the main central nervous system (CNS) neurotransmitters (dopamine: 9; noradrenaline: 19; serotonin: 23; inhibitory neurotransmitters: 60) in 889 individuals (417 men and 472 women) aged 18 years or older (585 psychiatric controls without a history of suicide attempts, and 304 patients with a history of suicide attempts). Individual analysis of association between single nucleotide polymorphisms and suicide attempts was estimated using logistic regression models.

RESULTS

Multifactor Dimensionality Reduction showed significant epistatic interactions involving four single nucleotide polymorphisms in female suicide attempters with a classification test accuracy of 60.7% (59.1%-62.4%, 95% CI): rs1522296, phenylalanine hydroxylase gene (PAH); rs7655090, dopamine receptor D5 gene (DRD5); rs11888528, chromosome 2 open reading frame 76, close to diazepam binding inhibitor gene (DBI); and rs2376481, GABA-A receptor subunit γ3 gene (GABRG3). The multivariate logistic regression model confirmed the relevance of the epistatic interaction [OR(95% CI)=7.74(4.60-13.37)] in females.

CONCLUSIONS

Our results suggest an epistatic interaction between genes of all monoamines and GABA in female suicide attempters.