Predicting death by suicide with administrative health care system data

Quantifying suicide risk with risk scales is common in clinical practice, but the performance of risk scales has been shown to be limited. Prediction models have been developed to quantify suicide risk and have been  shown to outperform risk scales, but these models have not been commonly adopted in clinical practice. The original research presented […]

Predicting death by suicide using administrative health care system data: Can recurrent neural network, one-dimensional convolutional neural network, and gradient boosted trees models improve prediction performance?

Background Suicide is a leading cause of death, particularly in younger persons, and this results in tremendous years of life lost. Objective To compare the performance of recurrent neural networks, one-dimensional convolutional neural networks, and gradient boosted trees, with logistic regression and feedforward neural networks. Methods The modeling dataset contained 3548 persons that died by […]

Can cognition help predict suicide risk in patients with major depressive disorder? A machine learning study

Background Previous studies suggest that deficits in cognition may increase the risk of suicide. Our study aims to develop a machine learning (ML) algorithm-based suicide risk prediction model using cognition in patients with major depressive disorder (MDD). Methods Participants comprised 52 depressed suicide attempters (DSA) and 61 depressed non-suicide attempters (DNS), and 98 healthy controls […]

I just want to matter: Examining the role of anti-mattering in online suicide support communities using natural language processing

Annually, tens of thousands of lives are affected by suicide, including those who die by suicide, attempt non-fatal suicidal behaviors, and suffer from suicidal thoughts and ideation as well as friends and family affected by the loss of loved ones. Although a number of predictors of suicidal thoughts and behaviors have been examined in the […]

Machine learning for suicide prediction and prevention: Advances, challenges, and future directions (IN: Youth Suicide Prevention and Intervention, edited by J.P. Ackerman & L.M. Horowitz)

This chapter describes the role of machine learning in youth suicide prevention. Following a brief history of suicide prediction, research is reviewed demonstrating that machine learning can enhance suicide prediction beyond traditional clinical and statistical approaches. Strategies for internal and external model evaluation, methods for integrating model results into clinical decision-making processes, and ethical issues […]

Predicting non‑response to multimodal day clinic treatment in severely impaired depressed patients: A machine learning approach

A considerable number of depressed patients do not respond to treatment. Accurate prediction of non-response to routine clinical care may help in treatment planning and improve results. A longitudinal sample of N = 239 depressed patients was assessed at admission to multi-modal day clinic treatment, after six weeks, and at discharge. First, patient’s treatment response was modelled […]

A direct comparison of theory-driven and machine learning prediction of suicide: A meta-analysis

Theoretically-driven models of suicide have long guided suicidology; however, an approach employing machine learning models has recently emerged in the field. Some have suggested that machine learning models yield improved prediction as compared to theoretical approaches, but to date, this has not been investigated in a systematic manner. The present work directly compares widely researched […]

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

Background Machine learning (ML) is increasingly used to predict suicide deaths but their value for suicide prevention has not been established. Our first objective was to identify risk and protective factors in a general population. Our second objective was to identify factors indicating imminent suicide risk. Methods We used survival and ML models to identify […]

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 […]

Predicting suicidal thoughts and behavior among adolescents using the risk and protective factor framework: A large-scale machine learning approach

Addressing the problem of suicidal thoughts and behavior (STB) in adolescents requires understanding the associated risk factors. While previous research has identified individual risk and protective factors associated with many adolescent social morbidities, modern machine learning approaches can help identify risk and protective factors that interact (group) to provide predictive power for STB. This study […]

Prediction of recurrent suicidal behavior among suicide attempters with Cox regression and machine learning: A 10-year prospective cohort study

Background Research on predictors and risk of recurrence after suicide attempt from China is lacking. This study aims to identify risk factors and develop prediction models for recurrent suicidal behavior among suicide attempters using Cox proportional hazard (CPH) and machine learning methods. Methods The prospective cohort study included 1103 suicide attempters with a maximum follow-up of 10 years from […]

Suicide risk and protective factors in online support forum posts: Annotation scheme development and validation study

Objective:his qualitative study aims to develop a valid and reliable annotation scheme for evaluating risk and protective factors for suicidal ideation in posts in suicide crisis forums. Methods: We designed a valid, reliable, and clinically grounded process for identifying risk and protective markers in social media data. This scheme draws on prior work on construct […]

Identifying socio-demographic risk factors for suicide using data on an individual level

Background Suicide is a complex issue. Due to the relative rarity of the event, studies into risk factors are regularly limited by sample size or biased samples. The aims of the study were to find risk factors for suicide that are robust to intercorrelation, and which were based on a large and unbiased sample. Methods […]

Designing a clinical decision support tool that leverages machine learning for suicide risk prediction: Development study in partnership with Native American care providers

Background: Machine learning algorithms for suicide risk prediction have been developed with notable improvements in accuracy. Implementing these algorithms to enhance clinical care and reduce suicide has not been well studied. Objective: This study aims to design a clinical decision support tool and appropriate care pathways for community-based suicide surveillance and case management systems operating […]

A machine learning approach for predicting suicidal thoughts and behaviours among college students

Suicidal thoughts and behaviours are prevalent among college students. Yet little is known about screening tools to identify students at higher risk. We aimed to develop a risk algorithm to identify the main predictors of suicidal thoughts and behaviours among college students within one-year of baseline assessment. We used data collected in 2013–2019 from the […]

New directions in machine learning analyses of administrative data to prevent suicide-related behaviors

This issue contains a thoughtful report by Gradus et al. on a machine learning (ML) analysis of administrative variables to predict suicide attempts over two decades throughout Denmark. This is one of numerous recent studies that document strong concentration of risk of suicide-related behaviors (SRBs) among patients with high scores on ML models. The clear […]

The use of closed-circuit television and video in suicide prevention: Narrative review and future directions

Background: Suicide is a recognized public health issue, with approximately 800,000 people dying by suicide each year. Among the different technologies used in suicide research, closed-circuit television (CCTV) and video have been used for a wide array of applications, including assessing crisis behaviors at metro stations, and using computer vision to identify a suicide attempt […]

Patients at high risk of suicide before and during a COVID-19 lockdown: Ecological momentary assessment study

The coronavirus disease 2019 (COVID-19) outbreak may have affected the mental health of patients at high risk of suicide. In this study we explored the wish to die and other suicide risk factors using smartphone-based ecological momentary assessment (EMA) in patients with a history of suicidal thoughts and behaviour. Contrary to our expectations we found […]

Can machine-learning methods really help predict suicide?

Purpose of review: In recent years there has been interest in the use of machine learning in suicide research in reaction to the failure of traditional statistical methods to produce clinically useful models of future suicide. The current review summarizes recent prediction studies in the suicide literature including those using machine learning approaches to understand what […]

Identification of suicide attempt risk factors in a national US survey using machine learning

Objective  To identify future suicide attempt risk factors in the general population using a data-driven machine learning approach including more than 2500 questions from a large, nationally representative survey of US adults. Design, Setting, and Participants  Data came from wave 1 (2001 to 2002) and wave 2 (2004 to 2005) of the National Epidemiologic Survey on Alcohol […]

Development of a machine learning model using multiple, heterogeneous data sources to estimate weekly US suicide fatalities

Objective  To estimate weekly suicide fatalities in the US in near real time. Design, Setting, and Participants  This cross-sectional national study used a machine learning pipeline to combine signals from several streams of real-time information to estimate weekly suicide fatalities in the US in near real time. This 2-phase approach first fits optimal machine learning models to […]

Suicide risk assessment using machine learning and social networks: A scoping review

According to the World Health Organization (WHO) report in 2016, around 800,000 of individuals have committed suicide. Moreover, suicide is the second cause of unnatural death in people between 15 and 29 years. This paper reviews state of the art on the literature concerning the use of machine learning methods for suicide detection on social networks. […]

Using machine learning to identify suicide risk: A classification tree approach to prospectively identify adolescent suicide attempters

This study applies classification tree analysis to prospectively identify suicide attempters among a large adolescent community sample, to demonstrate the strengths and limitations of this approach for risk identification. Data were drawn from the National Longitudinal Study of Adolescent to Adult Health. Youth (n = 4,834, Mage = 16.15, SD = 1.63, 52.3% female, 63.7% White) […]