Machine learning in suicide science: Applications and ethics

Prediction of first attempt of suicide in early adolescence using machine learning

BACKGROUND

Suicide is the second leading cause of death among early adolescents, yet the first onset of suicide attempts during this critical developmental period remains poorly understood. This study aimed to identify key characteristics associated with the first suicide attempt in early adolescence and to develop a predictive model for assessing individual risk.

METHODS

We used data from the Adolescent Brain Cognitive Development Study, a longitudinal, population-based study in the US. The analysis focused on a cohort of 4,238 early adolescents (aged 11-12 years) who had no prior history of suicide attempts. To predict the onset of a first suicide attempt over the subsequent two years (2020-2022), we developed an extremely randomized tree model, incorporating 87 potential predictors from diverse bio-psycho-social domains pertinent to adolescent development.

RESULTS

Among the 4,238 adolescents, 163 (3.8%) reported their first suicide attempt within the subsequent two years. Our predictive model demonstrated good discriminative ability, achieving an AUC of 0.82 (95% CI [0.79, 0.85]), with a sensitivity of 0.82 and a specificity of 0.69 at the optimized threshold. Key predictors included sex assigned at birth, sexual orientation, negative affect, internalizing and attention problems, and lifetime suicidal ideation, along with other significant factors from multiple domains.

CONCLUSIONS

These findings highlight the utility of machine learning algorithms in identifying predictors of suicide attempts among early adolescents. The insights gained from this study may contribute to the development of tailored screening tools and preventive interventions aimed at mitigating suicide risk in this vulnerable population.

Evaluating the ability of artificial intelligence to predict suicide: A systematic review of reviews

INTRODUCTION

Suicide remains a critical global public health issue, with approximately 800,000 deaths annually. Despite various prevention efforts, suicide rates are rising, highlighting the need for more effective strategies. Traditional suicide risk assessment methods often fall short in accuracy and predictive capability. This has driven interest in artificial intelligence (AI), particularly machine learning (ML), as a potential solution. This paper reviews systematic evaluations of AI's effectiveness in predicting suicide risk, aiming to explore AI's potential while addressing its challenges and limitations.

METHODOLOGY

A meta-research approach was used to review existing systematic reviews on AI's role in suicide risk prediction. Following PRISMA guidelines, a comprehensive search was conducted in PubMed and Web of Science for publications from 2004 to 2024. Relevant studies were selected based on specific inclusion criteria, and data were extracted on review characteristics, AI techniques, outcomes, and methodological quality. The review focuses on AI/ML models predicting suicidal ideation (SI), suicide attempts (SA), and suicide deaths (SD) separately, excluding non-suicidal self-injury.

RESULTS

Out of 96 initial articles, 23 met the inclusion criteria for full-text review. Most studies focused on developing ML models to identify suicide risk, showing promising results in enhancing accuracy and effectiveness. These models utilize various data sources and analytical techniques. However, challenges remain, including high bias risk and issues with interpretability, which necessitate further validation and refinement of AI-driven methods.

CONCLUSION

The review underscores the significant potential of AI, especially ML, in predicting suicide risk and attempts. Although ML models show promise, challenges like data limitations, bias, and interpretability issues need addressing. Continued research and ethical scrutiny are crucial to fully realize AI's potential in suicide prevention.

The Role of Despair in Predicting Self-Destructive Behaviors

Working age (25-64) mortality in the US has been increasing for decades, driven in part by rising deaths due to drug overdose, as well as increases in suicide and alcohol-related mortality. These deaths have been hypothesized by some to be due to despair, but this has rarely been empirically tested. For despair to explain mortality due to alcohol-related liver disease, suicide, and drug overdose, it must first predict the behaviors that lead to such causes of death. To that end, we aim to answer two research questions. First, does despair predict the behaviors that are antecedent to the "deaths of despair"? Second, what measures and domains of despair are most important? We use data from over 6000 individuals at five waves of the National Longitudinal Study of Adolescent to Adult Health and apply supervised machine learning to assess the role of despair in predicting self-destructive behaviors associated with these causes of death. Comparing predictive performance within each outcome using measures of despair to benchmark models of clinical and prior behavioral predictors, we evaluate the added predictive value of despair above and beyond established risk factors. We find that despair underperforms compared to clinical risk factors for suicidal ideation and heavy drinking, but over performs compared to clinical risk factors and prior behaviors for illegal drug use and prescription drug misuse. We also compare model performance and feature importance across outcomes; our ability to predict thoughts of suicide, drug abuse and misuse, and heavy drinking differs depending on the behavior, and the relative importance of different indicators of despair varies across outcomes as well. Our findings suggest that the self-destructive behaviors are distinct and the pathways from despair to self-destructive behavior varied. The results draw into question the relevance of despair as a unifying framework for understanding the current crisis in midlife health and mortality.

Acoustic Features for Identifying Suicide Risk in Crisis Hotline Callers: Machine Learning Approach

BACKGROUND

Crisis hotlines serve as a crucial avenue for the early identification of suicide risk, which is of paramount importance for suicide prevention and intervention. However, assessing the risk of callers in the crisis hotline context is constrained by factors such as lack of nonverbal communication cues, anonymity, time limits, and single-occasion intervention. Therefore, it is necessary to develop approaches, including acoustic features, for identifying the suicide risk among hotline callers early and quickly. Given the complicated features of sound, adopting artificial intelligence models to analyze callers' acoustic features is promising.

OBJECTIVE

In this study, we investigated the feasibility of using acoustic features to predict suicide risk in crisis hotline callers. We also adopted a machine learning approach to analyze the complex acoustic features of hotline callers, with the aim of developing suicide risk prediction models.

METHODS

We collected 525 suicide-related calls from the records of a psychological assistance hotline in a province in northwest China. Callers were categorized as low or high risk based on suicidal ideation, suicidal plans, and history of suicide attempts, with risk assessments verified by a team of 18 clinical psychology raters. A total of 164 clearly categorized risk recordings were analyzed, including 102 low-risk and 62 high-risk calls. We extracted 273 audio segments, each exceeding 2 seconds in duration, which were labeled by raters as containing suicide-related expressions for subsequent model training and evaluation. Basic acoustic features (eg, Mel Frequency Cepstral Coefficients, formant frequencies, jitter, shimmer) and high-level statistical function (HSF) features (using OpenSMILE [Open-Source Speech and Music Interpretation by Large-Space Extraction] with the ComParE 2016 configuration) were extracted. Four supervised machine learning algorithms (logistic regression, support vector machine, random forest, and extreme gradient boosting) were trained and evaluated using grouped 5-fold cross-validation and a test set, with performance metrics, including accuracy, F1-score, recall, and false negative rate.

RESULTS

The development of machine learning models utilizing HSF acoustic features has been demonstrated to enhance recognition performance compared to models based solely on basic acoustic features. The random forest classifier, developed with HSFs, achieved the best performance in detecting the suicide risk among the models evaluated (accuracy=0.75, F1-score=0.70, recall=0.76, false negative rate=0.24).

CONCLUSIONS

The results of our study demonstrate the potential of developing artificial intelligence-based early warning systems using acoustic features for identifying the suicide risk among crisis hotline callers. Our work also has implications for employing acoustic features to identify suicide risk in salient voice contexts.

Using machine learning methods to investigate the impact of age on the causes of death in patients with early intrahepatic cholangiocarcinoma who underwent surgery

BACKGROUND

The impact of age on the causes of death (CODs) in patients with early-stage intrahepatic cholangiocarcinoma (ICC) who had undergone surgery was analyzed in this study.

METHODS

A total of 1555 patients (885 in the older group and 670 in the younger group) were included in this study. Before and after applying inverse probability of treatment weighting (IPTW), the different CODs in the 2 groups were further investigated. Additionally, 7 different machine learning models were used as predictive tools to identify key variables, aiming to evaluate the therapeutic outcome in early ICC patients undergoing surgery.

RESULTS

Before (5.92 vs. 4.08 years, P < 0.001) and after (6.00 vs. 4.08 years, P < 0.001) IPTW, the younger group consistently showed longer overall survival (OS) compared with the older group. Before IPTW, there were no significant differences in cholangiocarcinoma-related deaths (CRDs, P = 0.7) and secondary malignant neoplasms (SMNs, P = 0.78) between the 2 groups. However, the younger group had a lower cumulative incidence of cardiovascular disease (CVD, P = 0.006) and other causes (P < 0.001) compared with the older group. After IPTW, there were no differences between the 2 groups in CRDs (P = 0.2), SMNs (P = 0.7), and CVD (P = 0.1). However, the younger group had a lower cumulative incidence of other CODs compared with the older group (P < 0.001). The random forest (RF) model showed the highest C-index of 0.703. Time-dependent variable importance bar plots showed that age was the most important factor affecting the 2-, 4-, and 6-year survival, followed by stage and size.

CONCLUSIONS

Our study confirmed that younger patients have longer OS compared with older patients. Further analysis of the CODs indicated that older patients are more likely to die from CVDs. The RF model demonstrated the best predictive performance and identified age as the most important factor affecting OS in early ICC patients undergoing surgery.

A Longitudinal Prediction of Suicide Attempts in Borderline Personality Disorder: A Machine Learning Study

Borderline personality disorder (BPD) is associated with a high risk of suicide. Despite several risk factors being known, identifying vulnerable patients in clinical practice remains a challenge so far. The current study aimed at predicting suicide attempts among BPD patients during disorder-specific psychotherapeutic interventions exploiting machine learning techniques. The study took into account several potential predictors relevant to BPD psychopathology: emotion dysregulation, temperamental and character factors, attachment style, impulsivity, and aggression. The sample included 69 patients with BPD who completed the Temperament and Character Inventory, Attachment Style Questionnaire, Difficulties in Emotion Regulation Scale, Barratt Impulsiveness Scale, and Aggression Questionnaire at baseline and after 6 months of psychotherapy. To detect future suicide attempts, baseline questionnaires were entered as predictors into an elastic net penalized regression, whose predictive performance was assessed through nested fivefold cross-validation. At the same time, 5000 iterations of a non-parametric bootstrap were used to determine predictors' robustness. The elastic net model discriminating BPD suicide attempters from non-attempters reached a balanced accuracy of 64.09% and an area under the receiver operating curve of 70.44%. High preoccupation with relationships, harm avoidance, and reward dependence, along with low motor impulsiveness, verbal aggression, cooperativeness, and self-transcendence were the most contributing predictors. Our findings suggest that interpersonal vulnerability and internalizing factors are the strongest predictors of future suicide attempts in BPD. Machine learning on self-report psychological scales may be helpful to identify individuals at suicidal risk, potentially helping clinical settings to develop individualized preventive strategies.

Validation of a machine learning model for indirect screening of suicidal ideation in the general population

Suicide is among the leading causes of death worldwide and a concerning public health problem, accounting for over 700,000 registered deaths worldwide. However, suicide deaths are preventable with timely and evidence-based interventions, which are often low-cost. Suicidal tendencies range from passive thoughts to ideation and actions, with ideation strongly predicting suicide. However, current screening methods yield limited accuracy, impeding effective prevention. The primary goal of this study was to validate a machine-learning-based model for screening suicidality using indirect questions, developed on data collected during the early COVID-19 pandemic and to differentiate suicide risk among subgroups like age and gender. The detection of suicidal ideation (SI) was based on habits, demographic features, strategies for coping with stress, and satisfaction with three important aspects of life. The model performed on par with the earlier study, surprisingly generalizing well even with different characteristics of the underlying population, not showing any significant effect of the machine learning drift. The sample of 1199 respondents reported an 18.6% prevalence of SI in the past month. The presented model for indirect suicidality screening has proven its validity in different circumstances and timeframes, emphasizing its potential as a tool for suicide prevention and intervention in the general population.

Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review

BACKGROUND

Prevention of suicide is a global health priority. Approximately 800,000 individuals die by suicide yearly, and for every suicide death, there are another 20 estimated suicide attempts. Large language models (LLMs) hold the potential to enhance scalable, accessible, and affordable digital services for suicide prevention and self-harm interventions. However, their use also raises clinical and ethical questions that require careful consideration.

OBJECTIVE

This scoping review aims to identify emergent trends in LLM applications in the field of suicide prevention and self-harm research. In addition, it summarizes key clinical and ethical considerations relevant to this nascent area of research.

METHODS

Searches were conducted in 4 databases (PsycINFO, Embase, PubMed, and IEEE Xplore) in February 2024. Eligible studies described the application of LLMs for suicide or self-harm prevention, detection, or management. English-language peer-reviewed articles and conference proceedings were included, without date restrictions. Narrative synthesis was used to synthesize study characteristics, objectives, models, data sources, proposed clinical applications, and ethical considerations. This review adhered to the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) standards.

RESULTS

Of the 533 studies identified, 36 (6.8%) met the inclusion criteria. An additional 7 studies were identified through citation chaining, resulting in 43 studies for review. The studies showed a bifurcation of publication fields, with varying publication norms between computer science and mental health. While most of the studies (33/43, 77%) focused on identifying suicide risk, newer applications leveraging generative functions (eg, support, education, and training) are emerging. Social media was the most common source of LLM training data. Bidirectional Encoder Representations from Transformers (BERT) was the predominant model used, although generative pretrained transformers (GPTs) featured prominently in generative applications. Clinical LLM applications were reported in 60% (26/43) of the studies, often for suicide risk detection or as clinical assistance tools. Ethical considerations were reported in 33% (14/43) of the studies, with privacy, confidentiality, and consent strongly represented.

CONCLUSIONS

This evolving research area, bridging computer science and mental health, demands a multidisciplinary approach. While open access models and datasets will likely shape the field of suicide prevention, documenting their limitations and potential biases is crucial. High-quality training data are essential for refining these models and mitigating unwanted biases. Policies that address ethical concerns-particularly those related to privacy and security when using social media data-are imperative. Limitations include high variability across disciplines in how LLMs and study methodology are reported. The emergence of generative artificial intelligence signals a shift in approach, particularly in applications related to care, support, and education, such as improved crisis care and gatekeeper training methods, clinician copilot models, and improved educational practices. Ongoing human oversight-through human-in-the-loop testing or expert external validation-is essential for responsible development and use.

TRIAL REGISTRATION

OSF Registries osf.io/nckq7; https://osf.io/nckq7.

Assessment of non-fatal injuries among university students in Hainan: a machine learning approach to exploring key factors

Background

Injuries constitute a significant global public health concern, particularly among individuals aged 0-34. These injuries are affected by various social, psychological, and physiological factors and are no longer viewed merely as accidental occurrences. Existing research has identified multiple risk factors for injuries; however, they often focus on the cases of children or the older adult, neglecting the university students. Machine learning (ML) can provide advanced analytics and is better suited to complex, nonlinear data compared to traditional methods. That said, ML has been underutilized in injury research despite its great potential. To fill this gap, this study applies ML to analyze injury data among university students in Hainan Province. The purpose is to provide insights into developing effective prevention strategies. To explore the relationship between scores on the self-rating anxiety scale and self-rating depression scale and the risk of non-fatal injuries within 1 year, we categorized these scores into two groups using restricted cubic splines.

Methods

Chi-square tests and LASSO regression analysis were employed to filter factors potentially associated with non-fatal injuries. The Synthetic Minority Over-Sampling Technique (SMOTE) was applied to balance the dataset. Subsequent analyses were conducted using random forest, logistic regression, decision tree, and XGBoost models. Each model underwent 10-fold cross-validation to mitigate overfitting, with hyperparameters being optimized to improve performance. SHAP was utilized to identify the primary factors influencing non-fatal injuries.

Results

The Random Forest model has proved effective in this study. It identified three primary risk factors for predicting non-fatal injuries: being male, favorable household financial situation, and stable relationship. Protective factors include reduced internet time and being an only child in the family.

Conclusion

The study highlighted five key factors influencing non-fatal injuries: sex, household financial situation, relationship stability, internet time, and sibling status. In identifying these factors, the Random Forest, Logistic Regression, Decision Tree, and XGBoost models demonstrated varying effectiveness, with the Random Forest model exhibiting superior performance.

Risk and protective correlates of suicidality in the military health and well-being project

Suicidality disproportionately affects Veterans, and in 2020 the Military Health and Well-Being Project was conducted in part to study the link between risk and protective constructs with suicidality among Veterans. In the present study, we investigate the relative contribution of risk (i.e., military self-stigma, daily stress, combat exposure, substance use, traumatic brain injury, and moral injury) and protective constructs (i.e., social integration, social contribution, public service motivation, purpose and meaning, and help-seeking) with suicidality. Using cross-sectional Pearson correlation and linear regression models, we studied the independent and relative contribution of risk and protective correlates in a sample of 1469 Veterans (male: n = 985, 67.1 %; female: n = 476, 32.4 %; transgender, non-binary, prefer not to say: n = 8, 0.5 %). When we investigated protective constructs individually as well as simultaneously, social contribution (β = -0.39, t = -15.59, p < 0.001) was the strongest protective construct against suicidality. Social integration (β = -0.13, t = -4.88, p < 0.001) additionally accounted for significant reduction in suicidality when all protective constructs were considered together. When we investigated the contribution of risk constructs towards suicidality, moral injury was most strongly associated with suicidality (r = 0.519, p < 0.001), yet when studied simultaneously for their relative contribution none of the constructs accounted for a significant amount of the variance in suicidality (|t|s ≤ 1.98, ps ≥ 0.07). These findings suggest that among Veterans it is possible that social contribution is protective against suicidality and could be a possible treatment target for the prevention or reduction of suicidality among Veterans.

Anticipating influential factors on suicide outcomes through machine learning techniques: Insights from a suicide registration program in western Iran

Suicide is a global public health concern, with increasing rates observed in various regions, including Iran. This study focuses on the province of Hamadan, Iran, where suicide rates have been on the rise. The research aims to predict factors influencing suicide outcomes by leveraging machine learning techniques on the Hamadan Suicide Registry Program data collected from 2016 to 2017. The study employs Naïve Bayes and Random Forest algorithms, comparing their performance to logistic regression. Results highlight the superiority of the Random Forest model. Based on the variable importance and multiple logistic regression analyses, the most important determinants of suicide outcomes were identified as suicide method, age, and timing of attempts, income, and motivation. The findings emphasize the cultural context's impact on suicide methods and underscore the importance of tailoring prevention programs to address specific risk factors, especially for older individuals. This study contributes valuable insights for suicide prevention efforts in the region, advocating for context-specific interventions and further research to refine predictive models and develop targeted prevention strategies.

Early Detection of Mental Health Crises through Artifical-Intelligence-Powered Social Media Analysis: A Prospective Observational Study

BACKGROUND

The early detection of mental health crises is crucial for timely interventions and improved outcomes. This study explores the potential of artificial intelligence (AI) in analyzing social media data to identify early signs of mental health crises.

METHODS

We developed a multimodal deep learning model integrating natural language processing and temporal analysis techniques. The model was trained on a diverse dataset of 996,452 social media posts in multiple languages (English, Spanish, Mandarin, and Arabic) collected from Twitter, Reddit, and Facebook over 12 months. Its performance was evaluated using standard metrics and validated against expert psychiatric assessments.

RESULTS

The AI model demonstrated a high level of accuracy (89.3%) in detecting early signs of mental health crises, with an average lead time of 7.2 days before human expert identification. Performance was consistent across languages (F1 scores: 0.827-0.872) and platforms (F1 scores: 0.839-0.863). Key digital markers included linguistic patterns, behavioral changes, and temporal trends. The model showed varying levels of accuracy for different crisis types: depressive episodes (91.2%), manic episodes (88.7%), suicidal ideation (93.5%), and anxiety crises (87.3%).

CONCLUSIONS

AI-powered analysis of social media data shows promise for the early detection of mental health crises across diverse linguistic and cultural contexts. However, ethical challenges, including privacy concerns, potential stigmatization, and cultural biases, need careful consideration. Future research should focus on longitudinal outcome studies, ethical integration of the method with existing mental health services, and developing personalized, culturally sensitive models.

A systematic review on the application of machine learning models in psychometric questionnaires for the diagnosis of attention deficit hyperactivity disorder

Attention deficit hyperactivity disorder is one of the most prevalent neurodevelopmental disorders worldwide. Recent studies show that machine learning has great potential for the diagnosis of attention deficit hyperactivity disorder. The aim of the present article is to systematically review the scientific literature on machine learning studies for the diagnosis of attention deficit hyperactivity disorder, focusing on psychometric questionnaire tools. The Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines were adopted. The review protocol was registered in the PROSPERO database. A search was conducted in three databases-Web of Science Core Collection, Scopus and Pubmed-with the aim of identifying studies that apply ML techniques to support the diagnosis of attention deficit hyperactivity disorder. A total of 17 empirical studies were found that met the established inclusion criteria. The results showed that machine learning can be used to increase the accuracy of attention deficit hyperactivity disorder diagnosis. Machine learning techniques are useful and effective strategies that can complement traditional diagnostics in patients with attention deficit hyperactivity disorder.

Using recursive partitioning to predict presence and severity of suicidal ideation amongst college students

OBJECTIVE

Predicting the presence and severity of suicidal ideation in college students is important, as deaths by suicide amongst young adults have increased in the past 20 years.

PARTICIPANTS

We recruited college students (N = 5494) from ten universities across eight states.

METHOD

Participants answered three questionnaires related to lifetime and past month suicidal ideation, and an indicator of suicidal ideation in a DSM-5 symptom measure. We used recursive partitioning to predict the presence, absence, and severity, of suicidal ideation.

RESULTS

Recursive partitioning models varied in their accuracy and performance. The best-performing model consisted of predictors and outcomes measured by the DSM-5 Level 1 Cross-Cutting Symptom Measure. Sexual orientation was also an important predictor in most models.

CONCLUSIONS

A single measure of DSM-5 symptom severity may help universities understand suicide severity to promote targeted interventions. Though further work is needed, as similar scaling amongst predictors could have influenced the model.

Adolescent, Parent, and Provider Perceptions of a Predictive Algorithm to Identify Adolescent Suicide Risk in Primary Care

OBJECTIVE

To understand adolescent, parent, and provider perceptions of a machine learning algorithm for detecting adolescent suicide risk prior to its implementation primary care.

METHODS

We conducted semi-structured, qualitative interviews with adolescents (n = 9), parents (n = 12), and providers (n = 10; mixture of behavioral health and primary care providers) across two major health systems. Interviews were audio recorded and transcribed with analyses supported by use of NVivo. A codebook was developed combining codes derived inductively from interview transcripts and deductively from implementation science frameworks for content analysis.

RESULTS

Reactions to the algorithm were mixed. While many participants expressed privacy concerns, they believed the algorithm could be clinically useful for identifying adolescents at risk for suicide and facilitating follow-up. Parents' past experiences with their adolescents' suicidal thoughts and behaviors contributed to their openness to the algorithm. Results also aligned with several key Consolidated Framework for Implementation Research domains. For example, providers mentioned barriers inherent to the primary care setting such as time and resource constraints likely to impact algorithm implementation. Participants also cited a climate of mistrust of science and health care as potential barriers.

CONCLUSIONS

Findings shed light on factors that warrant consideration to promote successful implementation of suicide predictive algorithms in pediatric primary care. By attending to perspectives of potential end users prior to the development and testing of the algorithm, we can ensure that the risk prediction methods will be well-suited to the providers who would be interacting with them and the families who could benefit.

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.

Unveiling Adolescent Suicidality: Holistic Analysis of Protective and Risk Factors Using Multiple Machine Learning Algorithms

Adolescent suicide attempts are on the rise, presenting a significant public health concern. Recent research aimed at improving risk assessment for adolescent suicide attempts has turned to machine learning. But no studies to date have examined the performance of stacked ensemble algorithms, which are more suitable for low-prevalence conditions. The existing machine learning-based research also lacks population-representative samples, overlooks protective factors and their interplay with risk factors, and neglects established theories on suicidal behavior in favor of purely algorithmic risk estimation. The present study overcomes these shortcomings by comparing the performance of a stacked ensemble algorithm with a diverse set of algorithms, performing a holistic item analysis to identify both risk and protective factors on a comprehensive data, and addressing the compatibility of these factors with two competing theories of suicide, namely, The Interpersonal Theory of Suicide and The Strain Theory of Suicide. A population-representative dataset of 173,664 Norwegian adolescents aged 13 to 18 years (mean = 15.14, SD = 1.58, 50.5% female) with a 4.65% rate of reported suicide attempt during the past 12 months was analyzed. Five machine learning algorithms were trained for suicide attempt risk assessment. The stacked ensemble model significantly outperformed other algorithms, achieving equal sensitivity and a specificity of 90.1%, AUC of 96.4%, and AUCPR of 67.5%. All algorithms found recent self-harm to be the most important indicator of adolescent suicide attempt. Exploratory factor analysis suggested five additional risk domains, which we labeled internalizing problems, sleep disturbance, disordered eating, lack of optimism regarding future education and career, and victimization. The identified factors provided stronger support for The Interpersonal Theory of Suicide than for The Strain Theory of Suicide. An enhancement to The Interpersonal Theory based on the risk and protective factors identified by holistic item analysis is presented.

A Systematic Review of the Barriers to the Implementation of Artificial Intelligence in Healthcare

Artificial intelligence (AI) is expected to improve healthcare outcomes by facilitating early diagnosis, reducing the medical administrative burden, aiding drug development, personalising medical and oncological management, monitoring healthcare parameters on an individual basis, and allowing clinicians to spend more time with their patients. In the post-pandemic world where there is a drive for efficient delivery of healthcare and manage long waiting times for patients to access care, AI has an important role in supporting clinicians and healthcare systems to streamline the care pathways and provide timely and high-quality care for the patients. Despite AI technologies being used in healthcare for some decades, and all the theoretical potential of AI, the uptake in healthcare has been uneven and slower than anticipated and there remain a number of barriers, both overt and covert, which have limited its incorporation. This literature review highlighted barriers in six key areas: ethical, technological, liability and regulatory, workforce, social, and patient safety barriers. Defining and understanding the barriers preventing the acceptance and implementation of AI in the setting of healthcare will enable clinical staff and healthcare leaders to overcome the identified hurdles and incorporate AI technologies for the benefit of patients and clinical staff.

User Feedback on the Use of a Natural Language Processing Application to Screen for Suicide Risk in the Emergency Department

Suicide is the 10th leading cause of death in the USA and globally. Despite decades of research, the ability to predict who will die by suicide is still no better than 50%. Traditional screening instruments have helped identify risk factors for suicide, but they have not provided accurate predictive power for reducing death rates. Over the past decade, natural language processing (NLP), a form of machine learning (ML), has been used to identify suicide risk by analyzing language data. Recent work has demonstrated the successful integration of a suicide risk screening interview to collect language data for NLP analysis from patients in two emergency departments (ED) of a large healthcare system. Results indicated that ML/NLP models performed well identifying patients that came to the ED for suicide risk. However, little is known about the clinician's perspective of how a qualitative brief interview suicide risk screening tool to collect language data for NLP integrates into an ED workflow. This report highlights the feedback and observations of patient experiences obtained from clinicians using brief suicide screening interviews. The investigator used an open-ended, narrative interview approach to inquire about the qualitative interview process. Three overarching themes were identified: behavioral health workflow, clinical implications of interview probes, and integration of an application into provider patient experience. Results suggest a brief, qualitative interview method was feasible, person-centered, and useful as a suicide risk detection approach.

Predicting non-improvement of symptoms in daily mental healthcare practice using routinely collected patient-level data: a machine learning approach

Objectives

Anxiety and mood disorders greatly affect the quality of life for individuals worldwide. A substantial proportion of patients do not sufficiently improve during evidence-based treatments in mental healthcare. It remains challenging to predict which patients will or will not benefit. Moreover, the limited research available on predictors of treatment outcomes comes from efficacy RCTs with strict selection criteria which may limit generalizability to a real-world context. The current study evaluates the performance of different machine learning (ML) models in predicting non-improvement in an observational sample of patients treated in routine specialized mental healthcare.

Methods

In the current longitudinal exploratory prediction study diagnosis-related, sociodemographic, clinical and routinely collected patient-reported quantitative outcome measures were acquired during treatment as usual of 755 patients with a primary anxiety, depressive, obsessive compulsive or trauma-related disorder in a specialized outpatient mental healthcare center. ML algorithms were trained to predict non-response (< 0.5 standard deviation improvement) in symptomatic distress 6 months after baseline. Different models were trained, including models with and without early change scores in psychopathology and well-being and models with a trimmed set of predictor variables. Performance of trained models was evaluated in a hold-out sample (30%) as a proxy for unseen data.

Results

ML models without early change scores performed poorly in predicting six-month non-response in the hold-out sample with Area Under the Curves (AUCs) < 0.63. Including early change scores slightly improved the models' performance (AUC range: 0.68-0.73). Computationally-intensive ML models did not significantly outperform logistic regression (AUC: 0.69). Reduced prediction models performed similar to the full prediction models in both the models without (AUC: 0.58-0.62 vs. 0.58-0.63) and models with early change scores (AUC: 0.69-0.73 vs. 0.68-0.71). Across different ML algorithms, early change scores in psychopathology and well-being consistently emerged as important predictors for non-improvement.

Conclusion

Accurately predicting treatment outcomes in a mental healthcare context remains challenging. While advanced ML algorithms offer flexibility, they showed limited additional value compared to traditional logistic regression in this study. The current study confirmed the importance of taking early change scores in both psychopathology and well-being into account for predicting longer-term outcomes in symptomatic distress.

Are false positives in suicide classification models a risk group? Evidence for "true alarms" in a population-representative longitudinal study of Norwegian adolescents

Introduction

False positives in retrospective binary suicide attempt classification models are commonly attributed to sheer classification error. However, when machine learning suicide attempt classification models are trained with a multitude of psycho-socio-environmental factors and achieve high accuracy in suicide risk assessment, false positives may turn out to be at high risk of developing suicidal behavior or attempting suicide in the future. Thus, they may be better viewed as "true alarms," relevant for a suicide prevention program. In this study, using large population-based longitudinal dataset, we examine three hypotheses: (1) false positives, compared to the true negatives, are at higher risk of suicide attempt in future, (2) the suicide attempts risk for the false positives increase as a function of increase in specificity threshold; and (3) as specificity increases, the severity of risk factors between false positives and true positives becomes more similar.

Methods

Utilizing the Gradient Boosting algorithm, we used a sample of 11,369 Norwegian adolescents, assessed at two timepoints (1992 and 1994), to classify suicide attempters at the first time point. We then assessed the relative risk of suicide attempt at the second time point for false positives in comparison to true negatives, and in relation to the level of specificity.

Results

We found that false positives were at significantly higher risk of attempting suicide compared to true negatives. When selecting a higher classification risk threshold by gradually increasing the specificity cutoff from 60% to 97.5%, the relative suicide attempt risk of the false positive group increased, ranging from minimum of 2.96 to 7.22 times. As the risk threshold increased, the severity of various mental health indicators became significantly more comparable between false positives and true positives.

Conclusion

We argue that the performance evaluation of machine learning suicide classification models should take the clinical relevance into account, rather than focusing solely on classification error metrics. As shown here, the so-called false positives represent a truly at-risk group that should be included in suicide prevention programs. Hence, these findings should be taken into consideration when interpreting machine learning suicide classification models as well as planning future suicide prevention interventions for adolescents.

Predictors of suicide ideation among South Korean adolescents: A machine learning approach

BACKGROUND

The current study developed a predictive model for suicide ideation among South Korean (Korean) adolescents using a comprehensive set of factors across demographic, physical and mental health, academic, social, and behavioral domains. The aim of this study was to address the pressing public health concerns of adolescent suicide in Korea and the methodological limitations of suicidal research.

METHODS

This study used machine learning methods (decision tree, logistic regression, naive Bayes classifier) to improve the accuracy of predicting suicidal ideation and related factors among a nationally representative sample of Korean middle school students (N = 6666).

RESULTS

Factors within all domains, including demographic characteristics, physical and mental health, and academic, social, and behavioral, were important in predicting suicidal thoughts among Korean adolescents, with mental health being the most important factor.

LIMITATIONS

The predictive model of the current research does not infer causality, and there may have been some loss of information due to measurement issues.

CONCLUSIONS

Study results provide insights for taking a multidimensional approach when identifying adolescents at risk of suicide, which may be used to further address their needs through intervention programs within the school setting. Considering the cultural stigma attached to disclosing suicidal ideation and behavior, the current study proposes the need for a preventive screening process based on the observation and assessment of adolescents' general characteristics and experiences in everyday life.

A Stakeholder-Informed Ethical Framework to Guide Implementation of Suicide Risk Prediction Models Derived from Electronic Health Records

OBJECTIVE

Develop a stakeholder-informed ethical framework to provide practical guidance to health systems considering implementation of suicide risk prediction models.

METHODS

In this multi-method study, patients and family members participating in formative focus groups (n = 4 focus groups, 23 participants), patient advisors, and a bioethics consultant collectively informed the development of a web-based survey; survey results (n = 1,357 respondents) and themes from interviews with stakeholders (patients, health system administrators, clinicians, suicide risk model developers, and a bioethicist) were used to draft the ethical framework.

RESULTS

Clinical, ethical, operational, and technical issues reiterated by multiple stakeholder groups and corresponding questions for risk prediction model adopters to consider prior to and during suicide risk model implementation are organized within six ethical principles in the resulting stakeholder-informed framework. Key themes include: patients' rights to informed consent and choice to conceal or reveal risk (autonomy); appropriate application of risk models, data and model limitations and consequences including ambiguous risk predictors in opaque models (explainability); selecting actionable risk thresholds (beneficence, distributive justice); access to risk information and stigma (privacy); unanticipated harms (non-maleficence); and planning for expertise and resources to continuously audit models, monitor harms, and redress grievances (stewardship).

CONCLUSIONS

Enthusiasm for risk prediction in the context of suicide is understandable given the escalating suicide rate in the U.S. Attention to ethical and practical concerns in advance of automated suicide risk prediction model implementation may help avoid unnecessary harms that could thwart the promise of this innovation in suicide prevention. HIGHLIGHTSPatients' desire to consent/opt out of suicide risk prediction models.Recursive ethical questioning should occur throughout risk model implementation.Risk modeling resources are needed to continuously audit models and monitor harms.

Binge Eating, Purging, and Restriction Symptoms: Increasing Accuracy of Prediction Using Machine Learning

Eating disorders are severe mental illnesses characterized by the hallmark behaviors of binge eating, restriction, and purging. These disordered eating behaviors carry extreme impairment and medical complications, regardless of eating disorder diagnosis. Despite the importance of these disordered behaviors to every eating disorder diagnosis, our current models are not able to accurately predict behavior occurrence. The current study utilized machine learning to develop longitudinal predictive models of binge eating, purging, and restriction in an eating disorder sample (N = 60) using real-time intensive longitudinal data. Participants completed four daily assessments of eating disorder symptoms and emotions for 25 days on a smartphone (total data points per participant = 100). Using data, we were able to compute highly accurate prediction models for binge eating, restriction, and purging (.76-.96 accuracy). The ability to accurately predict the occurrence of binge eating, restriction, and purging has crucial implications for the development of preventative interventions for the eating disorders. Machine learning models may be able to accurately predict onset of problematic psychiatric behaviors leading to preventative interventions designed to disrupt engagement in such behaviors.

Multi-task learning to detect suicide ideation and mental disorders among social media users

Mental disorders and suicide are considered global health problems faced by many countries worldwide. Even though advancements have been made to improve mental wellbeing through research, there is room for improvement. Using Artificial Intelligence to early detect individuals susceptible to mental illness and suicide ideation based on their social media postings is one way to start. This research investigates the effectiveness of using a shared representation to automatically extract features between the two different yet related tasks of mental illness and suicide ideation detection using data in parallel from social media platforms with different distributions. In addition to discovering the shared features between users with suicidal thoughts and users who self-declared a single mental disorder, we further investigate the impact of comorbidity on suicide ideation and use two datasets during inference to test the generalizability of the trained models and provide satisfactory evidence to validate the increased predictive accurateness of suicide risk when using data from users diagnosed with multiple mental disorders compared to a single mental disorder for the mental illness detection task. Our results also demonstrate different mental disorders' impact on suicidal risk and discover a noticeable impact when using data from users diagnosed with Post-Traumatic Stress Disorder. We use multi-task learning (MTL) with soft and hard parameter sharing to produce state-of-the-art results for detecting users with suicide ideation who require urgent attention. We further improve the predictability of the proposed model by demonstrating the effectiveness of cross-platform knowledge sharing and predefined auxiliary inputs.

Understanding Suicide over the Life Course Using Data Science Tools within a Triangulation Framework

Suicide and suicidal behaviors are important global health concerns. Preventing suicide requires a nuanced understanding of the nature of suicide risk, both acutely during periods of crisis and broader variation over the lifespan. However, current knowledge of the sources of variation in suicide risk is limited due to methodological and conceptual challenges. New methodological approaches are needed to close the gap between research and clinical practice. This review describes the life course framework as a conceptual model for organizing the scientific study of suicide risk across in four major domains: social relationships, health, housing, and employment. In addition, this review discusses the utility of data science tools as a means of identifying novel, modifiable risk factors for suicide, and triangulation as an overarching approach to ensuring rigor in suicide research as means of addressing existing knowledge gaps and strengthening future research.

Prediction of suicidal ideation among Chinese college students based on radial basis function neural network

Background

Suicide is one of the leading causes of death for college students. The predictors of suicidal ideation among college students are inconsistent and few studies have systematically investigated psychological symptoms of college students to predict suicide. Therefore, this study aims to develop a suicidal ideation prediction model and explore important predictors of suicidal ideation among college students in China.

Methods

We recruited 1,500 college students of Sichuan University and followed up for 4 years. Demographic information, behavioral and psychological information of the participants were collected using computer-based questionnaires. The Radial Basis Function Neural Network (RBFNN) method was used to develop three suicidal ideation risk prediction models and to identify important predictive factors for suicidal ideation among college students.

Results

The incidence of suicidal ideation among college students in the last 12 months ranged from 3.00 to 4.07%. The prediction accuracies of all the three models were over 91.7%. The area under curve scores were up to 0.96. Previous suicidal ideation and poor subjective sleep quality were the most robust predictors. Poor self-rated mental health has also been identified to be an important predictor. Paranoid symptom, internet addiction, poor self-rated physical health, poor self-rated overall health, emotional abuse, low average annual household income per person and heavy study pressure were potential predictors for suicidal ideation.

Conclusions

The study suggested that the RBFNN method was accurate in predicting suicidal ideation. And students who have ever had previous suicidal ideation and poor sleep quality should be paid consistent attention to.

The determinants of investment fraud: A machine learning and artificial intelligence approach

Investment fraud continues to be a severe problem in the Canadian securities industry. This paper aims to employ machine learning algorithms and artificial neural networks (ANN) to predict investment in Canada. Data for this study comes from cases heard by the Investment Industry Regulatory Organization of Canada (IIROC) between June 2008 and December 2019. In total, 406 cases were collected and coded for further analysis. After data cleaning and pre-processing, a total of 385 cases were coded for further analysis. The machine learning algorithms and artificial neural networks were able to predict investment fraud with very good results. In terms of standardized coefficient, the top five features in predicting fraud are offender experience, retired investors, the amount of money lost, the amount of money invested, and the investors' net worth. Machine learning and artificial intelligence have a pivotal role in regulation because they can identify the risks associated with fraud by learning from the data they ingest to survey past practices and come up with the best possible responses to predict fraud. If used correctly, machine learning in the form of regulatory technology can equip regulators with the tools to take corrective actions and make compliance more efficient to safeguard the markets and protect investors from unethical investment advisors.

Analyzing Suicide Risk From Linguistic Features in Social Media: Evaluation Study

Background

Effective suicide risk assessments and interventions are vital for suicide prevention. Although assessing such risks is best done by health care professionals, people experiencing suicidal ideation may not seek help. Hence, machine learning (ML) and computational linguistics can provide analytical tools for understanding and analyzing risks. This, therefore, facilitates suicide intervention and prevention.

Objective

This study aims to explore, using statistical analyses and ML, whether computerized language analysis could be applied to assess and better understand a person’s suicide risk on social media.

Methods

We used the University of Maryland Suicidality Dataset comprising text posts written by users (N=866) of mental health–related forums on Reddit. Each user was classified with a suicide risk rating (no, low, moderate, or severe) by either medical experts or crowdsourced annotators, denoting their estimated likelihood of dying by suicide. In language analysis, the Linguistic Inquiry and Word Count lexicon assessed sentiment, thinking styles, and part of speech, whereas readability was explored using the TextStat library. The Mann-Whitney U test identified differences between at-risk (low, moderate, and severe risk) and no-risk users. Meanwhile, the Kruskal-Wallis test and Spearman correlation coefficient were used for granular analysis between risk levels and to identify redundancy, respectively. In the ML experiments, gradient boost, random forest, and support vector machine models were trained using 10-fold cross validation. The area under the receiver operator curve and F₁-score were the primary measures. Finally, permutation importance uncovered the features that contributed the most to each model’s decision-making.

Results

Statistically significant differences (P<.05) were identified between the at-risk (671/866, 77.5%) and no-risk groups (195/866, 22.5%). This was true for both the crowd- and expert-annotated samples. Overall, at-risk users had higher median values for most variables (authenticity, first-person pronouns, and negation), with a notable exception of clout, which indicated that at-risk users were less likely to engage in social posturing. A high positive correlation (ρ>0.84) was present between the part of speech variables, which implied redundancy and demonstrated the utility of aggregate features. All ML models performed similarly in their area under the curve (0.66-0.68); however, the random forest and gradient boost models were noticeably better in their F₁-score (0.65 and 0.62) than the support vector machine (0.52). The features that contributed the most to the ML models were authenticity, clout, and negative emotions.

Conclusions

In summary, our statistical analyses found linguistic features associated with suicide risk, such as social posturing (eg, authenticity and clout), first-person singular pronouns, and negation. This increased our understanding of the behavioral and thought patterns of social media users and provided insights into the mechanisms behind ML models. We also demonstrated the applicative potential of ML in assisting health care professionals to assess and manage individuals experiencing suicide risk.

Machine Learning and Criminal Justice: A Systematic Review of Advanced Methodology for Recidivism Risk Prediction

Recent evolution in the field of data science has revealed the potential utility of machine learning (ML) applied to criminal justice. Hence, the literature focused on finding better techniques to predict criminal recidivism risk is rapidly flourishing. However, it is difficult to make a state of the art for the application of ML in recidivism prediction. In this systematic review, out of 79 studies from Scopus and PubMed online databases we selected, 12 studies that guarantee the replicability of the models across different datasets and their applicability to recidivism prediction. The different datasets and ML techniques used in each of the 12 studies have been compared using the two selected metrics. This study shows how each method applied achieves good performance, with an average score of 0.81 for ACC and 0.74 for AUC. This systematic review highlights key points that could allow criminal justice professionals to routinely exploit predictions of recidivism risk based on ML techniques. These include the presence of performance metrics, the use of transparent algorithms or explainable artificial intelligence (XAI) techniques, as well as the high quality of input data.

Using Boosted Machine Learning to Predict Suicidal Ideation by Socioeconomic Status among Adolescents

(1) Background: This study aimed to use machine learning techniques to identify risk factors for suicidal ideation among adolescents and understand the association between these risk factors and socioeconomic status (SES); (2) Methods: Data from 54,948 participants were analyzed. Risk factors were identified by dividing groups by suicidal ideation and 3 SES levels. The influence of risk factors was confirmed using the synthetic minority over-sampling technique and XGBoost; (3) Results: Adolescents with suicidal thoughts experienced more sadness, higher stress levels, less happiness, and higher anxiety than those without. In the high SES group, academic achievement was a major risk factor for suicidal ideation; in the low SES group, only emotional factors such as stress and anxiety significantly contributed to suicidal ideation; (4) Conclusions: SES plays an important role in the mental health of adolescents. Improvements in SES in adolescence may resolve their negative emotions and reduce the risk of suicide.

Deep Learning-Based Text Emotion Analysis for Legal Anomie

Text emotion analysis is an effective way for analyzing the emotion of the subjects’ anomie behaviors. This paper proposes a text emotion analysis framework (called BCDF) based on word embedding and splicing. Bi-direction Convolutional Word Embedding Classification Framework (BCDF) can express the word vector in the text and embed the part of speech tagging information as a feature of sentence representation. In addition, an emotional parallel learning mechanism is proposed, which uses the temporal information of the parallel structure calculated by Bi-LSTM to update the storage information through the gating mechanism. The convolutional layer can better extract certain components of sentences (such as adjectives, adverbs, nouns, etc.), which play a more significant role in the expression of emotion. To take advantage of convolution, a Convolutional Long Short-Term Memory (ConvLSTM) network is designed to further improve the classification results. Experimental results show that compared with traditional LSTM model, the proposed text emotion analysis model has increased 3.3 and 10.9% F1 score on psychological and news text datasets, respectively. The proposed CBDM model based on Bi-LSTM and ConvLSTM has great value in practical applications of anomie behavior analysis.

A Machine Learning Approach for Predicting Wage Workers’ Suicidal Ideation

(1) Background: Workers spend most of their days working. One’s working environment can be a risk factor for suicide. In this study, we examined whether suicidal ideation can be predicted using individual characteristics, emotional states, and working environments. (2) Methods: Nine years of data from the Korean National Health and Nutrition Survey were used. A total of 12,816 data points were analyzed, and 23 variables were selected. The random forest technique was used to predict suicidal thoughts. (3) Results: When suicidal ideation cases were predicted using all of the independent variables, 98.9% of cases were predicted, and 97.4% could be predicted using only work-related conditions. (4) Conclusions: It was confirmed that suicide risk could be predicted efficiently when machine learning techniques were applied using variables such as working environments.

Suicide and self-harm

Suicide and self-harm are major health and societal issues worldwide, but the greatest burden of both behaviours occurs in low-income and middle-income countries. Although rates of suicide are higher in male than in female individuals, self-harm is more common in female individuals. Rather than having a single cause, suicide and self-harm are the result of a complex interplay of several factors that occur throughout the life course, and vary by gender, age, ethnicity, and geography. Several clinical and public health interventions show promise, although our understanding of their effectiveness has largely originated from high-income countries. Attempting to predict suicide is unlikely to be helpful. Intervention and prevention must include both a clinical and community focus, and every health professional has a crucial part to play.

The Effectiveness of Predicting Suicidal Ideation through Depressive Symptoms and Social Isolation Using Machine Learning Techniques

(1) Background: Social isolation is a major risk factor for suicidal ideation. In this study, we investigated whether the evaluation of both depression and social isolation in combination could effectively predict suicidal ideation; (2) Methods: A total of 7994 data collected from community residents were analyzed. Statistical analysis was performed using age, the Patient Health Questionnaire-9, and the Lubben Social Network Scale as predictors as the dependent variables for suicidal ideation; machine learning (ML) methods K-Nearest Neighbors, Random Forest, and Neural Network Classification were used; (3) Results: The prediction of suicidal ideation using depression and social isolation showed high area under the curve (0.643–0.836) and specificity (0.959–0.987) in all ML techniques. In the predictor model (model 2) that additionally evaluated social isolation, the validation accuracy consistently increased compared to the depression-only model (model 1); (4) Conclusions: It is confirmed that the machine learning technique using depression and social isolation can be an effective method when predicting suicidal ideation.

Translating promise into practice: a review of machine learning in suicide research and prevention

In ever more pressured health-care systems, technological solutions offering scalability of care and better resource targeting are appealing. Research on machine learning as a technique for identifying individuals at risk of suicidal ideation, suicide attempts, and death has grown rapidly. This research often places great emphasis on the promise of machine learning for preventing suicide, but overlooks the practical, clinical implementation issues that might preclude delivering on such a promise. In this Review, we synthesise the broad empirical and review literature on electronic health record-based machine learning in suicide research, and focus on matters of crucial importance for implementation of machine learning in clinical practice. The challenge of preventing statistically rare outcomes is well known; progress requires tackling data quality, transparency, and ethical issues. In the future, machine learning models might be explored as methods to enable targeting of interventions to specific individuals depending upon their level of need-ie, for precision medicine. Primarily, however, the promise of machine learning for suicide prevention is limited by the scarcity of high-quality scalable interventions available to individuals identified by machine learning as being at risk of suicide.

Integration and Validation of a Natural Language Processing Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in the Emergency Department

BACKGROUND

Emergency departments (ED) are an important intercept point for identifying suicide risk and connecting patients to care, however, more innovative, person-centered screening tools are needed. Natural language processing (NLP) -based machine learning (ML) techniques have shown promise to assess suicide risk, although whether NLP models perform well in differing geographic regions, at different time periods, or after large-scale events such as the COVID-19 pandemic is unknown.

OBJECTIVE

To evaluate the performance of an NLP/ML suicide risk prediction model on newly collected language from the Southeastern United States using models previously tested on language collected in the Midwestern US.

METHOD

37 Suicidal and 33 non-suicidal patients from two EDs were interviewed to test a previously developed suicide risk prediction NLP/ML model. Model performance was evaluated with the area under the receiver operating characteristic curve (AUC) and Brier scores.

RESULTS

NLP/ML models performed with an AUC of 0.81 (95% CI: 0.71-0.91) and Brier score of 0.23.

CONCLUSION

The language-based suicide risk model performed with good discrimination when identifying the language of suicidal patients from a different part of the US and at a later time period than when the model was originally developed and trained.

Machine Learning Based Healthcare System for Investigating the Association Between Depression and Quality of Life

New technological innovations are changing the future of healthcare system. Identification of factors that are responsible for causing depression may lead to new experiments and treatments. Because depression as a disease is becoming a leading community health concern worldwide. Using machine learning techniques this article presents a complete methodological framework to process and explore the heterogenous data and to better understand the association between factors related to quality of life and depression. Subsequently, the experimental study is mainly divided into two parts. In the first part, a data consolidation process is presented. The relationship of data is formed and to uniquely identify each relation in data the concept of the Secure Hash Algorithm is adopted. Hashing is used to locate and index the actual items in the data because it is easier to process short hash values instead of longer strings. The second part proposed a model using both unsupervised and supervised machine learning techniques. The consolidation approach helped in providing a base for formulation and validation of the research hypothesis. The Self organizing map provided 08 cluster solution and the classification problems were taken from the clustered data to further validate the performance of the posterior probability multi-class Support Vector Machine. The expectations of the importance sampling resulted in factors responsible for causing depression. The proposed model was adopted to improve the classification performance, and the result showed classification accuracy of 91.16%.

Hierarchical and nested associations of suicide with marriage, social support, quality of life, and depression among the elderly in rural China: Machine learning of psychological autopsy data

OBJECTIVES

To identify mechanisms underpinning the complex relationships between influential factors and suicide risk with psychological autopsy data and machine learning method.

DESIGN

A case-control study with suicide deaths selected using two-stage stratified cluster sampling method; and 1:1 age-and-gender matched live controls in the same geographic area.

SETTING

Disproportionately high risk of suicide among rural elderly in China.

PARTICIPANTS

A total of 242 subjects died from suicide and 242 matched live controls, 60 years of age and older.

MEASUREMENTS

Suicide death was determined based on the ICD-10 codes. Influential factors were measured using validated instruments and commonly accepted variables.

RESULTS

Of the total sample, 270 (55.8%) were male with mean age = 74.2 (SD = 8.2) years old. Four CART models were used to select influential factors using the criteria: areas under the curve (AUC) ≥ 0.8, sensitivity ≥ 0.8, and specificity ≥ 0.8. Each model included a lead predictor plus 8-10 hierarchically nested factors. Depression was the first to be selected in Model 1 as the lead predictor; After depression was excluded, quality of life (QOL) was selected in Model 2; After depression and QOL were excluded, social support was selected in Model 3. Finally, after all 3 lead factors were excluded, marital status was selected in Model 4. In addition, CART demonstrated the significance of several influential factors that would not be associated with suicide if the data were analyzed using the conventional logistic regression.

CONCLUSION

Associations between the key factors and suicide death for Chinese rural elderly are not linear and parallel but hierarchically nested that could not be effectively detected using conventional statistical methods. Findings of this study provide new and compelling evidence supporting tailored suicide prevention interventions at the familial, clinical and community levels.

Editorial: Disrupting Pathways to Self-Harm in Adolescence: Machine Learning as an Opportunity

Self-harm, hurting oneself with or without suicidal intent, is associated with poor mental health. Domains of risk known to be associated with self-harm include sociodemographic factors such as female gender, negative life events, family adversity, and psychiatric diagnoses.¹ However, the heterogeneous nature of self-harm makes predicting risk and prevention challenging. The behaviors can be occasional or repetitive, suicidal in nature or not. Only about half of youths with deliberate self-harm present significant suicide risk.¹ We are left with these remaining questions: What are the early signs of risk for self-harm? Who are the children and adolescents most at risk? Machine learning is the scientific discipline that focuses on how computers learn from data with efficient computing algorithms and prediction models.² If we can use this analytic tool wisely, it could help us to predict risk of self-injury and offer prevention and treatment with precision. However, we need to be careful not to replicate the human biases that already permeate our health care system by failing to include data from diverse populations or considering the ways they are marginalized in building prediction models.

Advances in Research on Adolescent Suicide and a High Priority Agenda for Future Research

Suicide is the second leading cause of death for adolescents in the United States, yet remarkably little is known regarding risk factors for suicidal thoughts and behaviors (STBs), relatively few federal grants and scientific publications focus on STBs, and few evidence-based approaches to prevent or treat STBs are available. This "decade in review" article discusses five domains of recent empirical findings that span biological, environmental, and contextual systems and can guide future research in this high priority area: (1) the role of the central nervous system; (2) physiological risk factors, including the peripheral nervous system; (3) proximal acute stress responses; (4) novel behavioral and psychological risk factors; and (5) broader societal factors impacting diverse populations and several additional nascent areas worthy of further investigation.

Comparison of traditional model-based statistical methods with machine learning for the prediction of suicide behaviour

BACKGROUND

Despite considerable research efforts during the last five decades, the prediction of suicidal behaviour (SB) using traditional model-based statistical has been weak. This marks the need to explore new statistical methods.

OBJECTIVE

To compare the performance of Cox regression models versus Random Survival Forest (RSF) to predict SB.

METHODS

Using a data set of more than 300 high-risk suicidal patients from a multicenter prospective cohort study, we compare Cox regression models with RSF to address predictors of time to suicide reattempt. Cross-validation was used to assess model prediction performance, including the area under the receiver operator curve (AUC), precision, Integrated Brier Score (IBS), sensitivity, and specificity.

RESULTS

A variant of the RSF denominated the RSFElimin, in which irrelevant predictor variables were eliminated from the model, presented the best accuracy, sensitivity, AUC and IBS. At the same time, the sensitivity of this method was slightly lower than that obtained with the Cox regression model with all predictor variables (CoxComp).

CONCLUSION

The RSF, a machine learning model, seems more sensitive and precise than the traditional Cox regression model in predicting suicidal behaviour.

Genetic testing for suicide risk assessment: Theoretical premises, research challenges and ethical concerns

We explore ethical premises and practical implications of using genetic testing to predict suicide risk. Twin studies indicate heritable components of suicide risk, intertwined with the heritability of mental disorders, and possibly other traits. Current genetics research has abandoned searching for single gene Mendelian determinants, in favour of complex probabilistic epigenetic models. Genome-Wide Association Studies (GWAS) might identify thousands of single nucleotide polymorphisms (SNPs), each contributing very little to the variance associated with behavioral phenotypes. However, suicide is a behavioral outcome rather than a phenotype, with so many different causal aetiologies, that it is impossible to predict the behaviors of individuals. We analyse practical and ethical issues that would arise if future research were to identify genetic information that will accurately predict suicide. Applying ACCE guidelines that specify when genetic tests should and should not be used, we examine the Analytic Validity, Clinical Validity, Clinical Utility and Ethical, Legal, and Social Implications. Low sensitivity and specificity for predicting suicide diminish potential advantages and exacerbate risks. Key considerations include the likelihood that testing will result in effective preventive interventions, which are not currently available, and unreliable positive results increasing hopelessness, stigma, and psychosocial risks. If the unregulated direct-to-consumer genetic testing services include suicide risk assessments, their use risks negative impacts. In the future, if genetic testing could accurately identify suicide risk in individuals, its use would be contraindicated if we cannot provide effective preventive interventions and mitigate the negative impacts of informing people about their risk level.