Frequency of clinicians’ assessments for access to lethal means in persons at risk for suicide

Objective We measured the frequency of clinicians’ assessments for access to lethal means, including firearms and medications in patients at risk of suicide from electronic medical and mental health records in outpatient and emergency settings. Methods We included adult patients who reported suicide ideation on the PHQ-9 depression screener in behavioral health and primary care […]

Using natural language processing to extract self-harm and suicidality data from a clinical sample of patients with eating disorders: A retrospective cohort study

Objectives: The objective of this study was to determine risk factors for those diagnosed with eating disorders who report self-harm and suicidality. Design and setting: This study was a retrospective cohort study within a secondary mental health service, South London and Maudsley National Health Service Trust. Participants: All diagnosed with an F50 diagnosis of eating disorder from January […]

Pandora’s bot: Insights from the syntax and semantics of suicide notes (IN: Healthier Lives, Digitally Enabled, edited by M. Merolli, C. Bain & L.K. Schaper)

Conversation agents (chat-bots) are becoming ubiquitous in many domains of everyday life, including physical and mental health and wellbeing. With the high rate of suicide in Australia, chat-bot developers are facing the challenge of dealing with statements related to mental ill-health, depression and suicide. Advancements in natural language processing could allow for sensitive, considered responses, […]

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 […]

Trends in reasons for emergency calls during the COVID-19 crisis in the department of Gironde, France using artificial neural network for natural language classification

Objectives During periods such as the COVID-19 crisis, there is a need for responsive public health surveillance indicators in order to monitor both the epidemic growth and potential public health consequences of preventative measures such as lockdown. We assessed whether the automatic classification of the content of calls to emergency medical communication centers could provide […]

Job-related problems prior to nurse suicide, 2003-2017: A mixed methods analysis using natural language processing and thematic analysis

Background Nurses have a higher rate of suicide than the gender-matched general population at baseline. Quantitative data from the Centers for Disease Control and Prevention National Violent Death Reporting System have been previously analyzed to reveal that nurses have more known job-related issues prior to death by suicide. However, no known study has focused on […]

Cross-lingual suicidal-oriented word embedding toward suicide prevention

Early intervention for suicide risks with social media data has increasingly received great attention. Using a suicide dictionary created by mental health experts is one of the effective ways to detect suicidal ideation. However, little attention has been paid to validate whether and how the existing dictionaries for other languages (i.e., English and Chinese) can […]

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. […]

Identifying and predicting intentional self-harm in electronic health record clinical notes: Deep learning approach

Background: Suicide is an important public health concern in the United States and around the world. There has been significant work examining machine learning approaches to identify and predict intentional self-harm and suicide using existing data sets. With recent advances in computing, deep learning applications in health care are gaining momentum. Objective: This study aimed to […]

Deep neural networks detect suicide risk from textual Facebook posts

Background: Detection of suicide risk is a highly prioritized, yet complicated task. In fact, five decades of suicide research produced predictions that were only marginally better than chance (AUCs = 0.56 – 0.58). Advanced machine learning methods open up new opportunities for progress in mental health research. In the present study, Artificial Neural Network (ANN) […]

Predicting user emotional tone in mental disorder online communities

Online Social Networks have become an important medium for communication among people who suffer from mental disorders to share moments of hardship and to seek support. Here we analyze how Reddit discussions can help improve the health conditions of its users. Using emotional tone of user publications as a proxy for his emotional state, we […]

Detection of suicidal intent in Spanish language social networks using machine learning

Suicide is a considerable problem in our population, early intervention for its prevention has a very important role, in order to counteract the number of deaths from suicide. Today, just over half of the world’s population uses social networks, where they express ideas, feelings, desires, including suicide intentions. Motivated by these factors, the main objective […]

Clinician-recalled quoted speech in electronic health records and risk of suicide attempt: A case-crossover study

Objective: Clinician narrative style in electronic health records (EHR) has rarely been investigated. Clinicians sometimes record brief quotations from patients, possibly more frequently when higher risk is perceived. We investigated whether the frequency of quoted phrases in an EHR was higher in time periods closer to a suicide attempt. Design: A case-crossover study was conducted in a […]

Comparing automatically extracted topics from online suicidal ideation and the responses they invoke

Suicide is a national public health concern, claiming over one million lives each year worldwide. The ability to understand, identify, and respond to suicidal behavior remains a key priority in preventing suicide. As online social networks have grown in accessibility and popularity, it is increasingly common for users to both discuss mental health and receive […]

Testing out suicide risk prediction algorithms using phone measurements with patients in acute mental health settings: A feasibility study

Background: Digital phenotyping and machine learning are nowadays being used to augment or even replace traditional analytic procedures in many domains, including health care. Given the heavy reliance on smartphones and mobile devices around the world, this readily available source of data is an important and highly underutilized source that has the potential to improve […]

Descriptive analysis of suicide ideation on Twitter

The start of the online social networks have caused an spike in interactions between people. While this has a positive impact in society, it can also spread the ideations of suicide leading to the contagion effect.We used live twitter data for our database along with previously labelled twitter data and draw conclusions about the data […]

Natural language processing of social media as screening for suicide risk

Suicide is among the 10 most common causes of death, as assessed by the World Health Organization. For every death by suicide, an estimated 138 people’s lives are meaningfully affected, and almost any other statistic around suicide deaths is equally alarming. The pervasiveness of social media—and the near-ubiquity of mobile devices used to access social […]

Improving prediction of suicide and accidental death after discharge from general hospitals with natural language processing

OBJECTIVE: To determine the extent to which incorporating natural language processing of narrative discharge notes improves stratification of risk for death by suicide after medical or surgical hospital discharge. DESIGN, SETTING, AND PARTICIPANTS: In this retrospective health care use study, clinical data were analyzed from individuals with discharges from 2 large academic medical centers between […]

Assessing suicide risk and emotional distress in Chinese social media: A text mining and machine learning study.

Background: Early identification and intervention are imperative for suicide prevention. However, at-risk people often neither seek help nor take professional assessment. A tool to automatically assess their risk levels in natural settings can increase the opportunity for early intervention. Objective: The aim of this study was to explore whether computerized language analysis methods can be […]

A controlled trial using natural language processing to examine the language of suicidal adolescents in the emergency department.

Journal copy held in CSP Library.

Sentiment analysis of suicide notes: A shared task

This paper reports on a shared task involving the assignment of emotions to suicide notes. Two features distinguished this task from previous shared tasks in the biomedical domain. One is that it resulted in the corpus of fully anonymized clinical text and annotated suicide notes. This resource is permanently available and will (we hope) facilitate […]