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 […]

Harnessing innovative technologies to train nurses in suicide safety planning with hospitalized patients: Protocol for formative and pilot feasibility research

Background: Suicide is the 10th leading cause of death in the United States, with >47,000 deaths in 2019. Most people who died by suicide had contact with the health care system in the year before their death. Health care provider  training is a top research priority identified by the National Action Alliance for Suicide Prevention; […]

Suicide and the agent-host-environment triad: Leveraging surveillance sources to inform prevention

Suicide in the US has increased in the last decade, across virtually every age and demographic group. Parallel increases have occurred in non-fatal self-harm as well. Research on suicide across the world has consistently demonstrated that suicide shares many properties with a communicable disease, including person-to-person transmission and point-source outbreaks. This essay illustrates the communicable […]

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 […]

Machine learning based prediction of suicide probability

Many factors have led to the increase of suicide-proneness in the present era. As a consequence, many novel methods have been proposed in recent times for prediction of the probability of suicides, using different metrics. The current work reviews a number of models and techniques proposed recently, and offers a novel Bayesian machine learning (ML) […]

The utility of artificial intelligence for mood analysis, depression detection, and suicide risk management

Mood disorders are often an indication or a sign of depression, and individuals suffering from mood swings may face higher probability and increased suicidal tendencies. Depression—also called “clinical depression” or a “depressive disorder”—is a mood disorder that adversely impacts how an individual feels, thinks, and handles daily activities, such as sleeping, eating, or working. To […]

Recognizing states of psychological vulnerability to suicidal behavior: A Bayesian network of artificial intelligence applied to a clinical sample

Background This study aimed to determine conditional dependence relationships of variables that contribute to psychological vulnerability associated with suicide risk. A Bayesian network (BN) was developed and applied to establish conditional dependence relationships among variables for each individual subject studied. These conditional dependencies represented the different states that patients could experience in relation to suicidal […]

Technology for our future? Exploring the duty to report and processes of subjectification relating to digitalized suicide prevention

Digital and networking technologies are increasingly used to predict who is at risk of attempting suicide. Such digitalized suicide prevention within and beyond mental health care raises ethical, social and legal issues for a range of actors involved. Here, I will draw on key literature to explore what issues (might) arise in relation to digitalized […]

Artificial intelligence-based suicide prediction

Suicidal thoughts and behaviors are an international public health problem contributing to 800,000 annual deaths and up to 25 million nonfatal suicide attempts. In the United States, suicide rates have increased steadily for two decades, reaching 47,000 per year and surpassing annual motor vehicle deaths. This trend has prompted government agencies, healthcare systems, and multinational […]

Predicting death by suicide following an emergency department visit for parasuicide with administrative health care system data and machine learning

Background Suicide is a leading cause of death worldwide and results in a large number of person years of life lost. There is an opportunity to evaluate whether administrative health care system data and machine learning can quantify suicide risk in a clinical setting. Methods The objective was to compare the performance of prediction models […]

Applying computer adaptive testing methods to suicide risk screening in the emergency department

Objective Combine test theory with technology to develop brief, reliable suicide risk measures in the emergency department. Methods A computer adaptive test for suicide risk was built using the Beck Scale for Suicide Ideation and tested among the emergency department population. Data were analyzed from a sample of 1,350 patients in several Massachusetts emergency departments. […]

Comparing indicators of suicidality among users in different types of nonprofessional suicide message boards: A linguistic analysis

Background: Little is known about linguistic differences between nonprofessional suicide message boards that differ in regard to their predominant attitude to suicide. Aims: To compare linguistic indicators potentially related to suicidality between anti-suicide, neutral, and pro-suicide message boards, and between the types of posters (primary posters, who initiate the thread, and the respective respondents). Method: […]

Do search engine helpline notices aid in preventing suicide? Analysis of archival data

Background: Search engines display helpline notices when people query for suicide-related information. Objective: In this study, we aimed to examine if these notices and other information displayed in response to suicide-related queries are correlated with subsequent searches for suicide prevention rather than harmful information. Methods: Anonymous suicide-related searches made on Bing and Google in the United States, the […]

Digital phenotyping of suicidal thoughts.

BACKGROUND: To examine whether there are subtypes of suicidal thinking using real-time digital monitoring, which allows for the measurement of such thoughts with greater temporal granularity than ever before possible. METHODS: We used smartphone-based real-time monitoring to assess suicidal thoughts four times per day in two samples: Adults who attempted suicide in the past year […]

A linguistic analysis of suicide-related twitter posts.

Background: Suicide is a leading cause of death worldwide. Identifying those at risk and delivering timely interventions is challenging. Social media site Twitter is used to express suicidality. Automated linguistic analysis of suicide-related posts may help to differentiate those who require support or intervention from those who do not. Aims: This study aims to characterize the linguistic profiles […]