AIMC Topic: Suicide Prevention

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A randomized controlled trial of suicide prevention training for primary care providers: a study protocol.

BMC medical education
BACKGROUND: Suicide is a national public health crisis and a critical patient safety issue. It is the 10th leading cause of death overall and the second leading cause of death among adolescents and young adults (15-34 years old). Research shows 80% o...

Machine learning in suicide science: Applications and ethics.

Behavioral sciences & the law
For decades, our ability to predict suicide has remained at near-chance levels. Machine learning has recently emerged as a promising tool for advancing suicide science, particularly in the domain of suicide prediction. The present review provides an ...

Toward Automatic Risk Assessment to Support Suicide Prevention.

Crisis
Suicide has been considered an important public health issue for years and is one of the main causes of death worldwide. Despite prevention strategies being applied, the rate of suicide has not changed substantially over the past decades. Suicide ri...

Psychiatric stressor recognition from clinical notes to reveal association with suicide.

Health informatics journal
Suicide takes the lives of nearly a million people each year and it is a tremendous economic burden globally. One important type of suicide risk factor is psychiatric stress. Prior studies mainly use survey data to investigate the association between...

Extracting psychiatric stressors for suicide from social media using deep learning.

BMC medical informatics and decision making
BACKGROUND: Suicide has been one of the leading causes of deaths in the United States. One major cause of suicide is psychiatric stressors. The detection of psychiatric stressors in an at risk population will facilitate the early prevention of suicid...

Smartphones, Sensors, and Machine Learning to Advance Real-Time Prediction and Interventions for Suicide Prevention: a Review of Current Progress and Next Steps.

Current psychiatry reports
PURPOSE OF REVIEW: As rates of suicide continue to rise, there is urgent need for innovative approaches to better understand, predict, and care for those at high risk of suicide. Numerous mobile and sensor technology solutions have already been propo...

A Machine Learning Approach to Identifying the Thought Markers of Suicidal Subjects: A Prospective Multicenter Trial.

Suicide & life-threatening behavior
Death by suicide demonstrates profound personal suffering and societal failure. While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought ma...

Identifying a clinical signature of suicidality among patients with mood disorders: A pilot study using a machine learning approach.

Journal of affective disorders
OBJECTIVE: A growing body of evidence has put forward clinical risk factors associated with patients with mood disorders that attempt suicide. However, what is not known is how to integrate clinical variables into a clinically useful tool in order to...

Analysis of NTSB Aircraft-Assisted Pilot Suicides: 1982-2014.

Suicide & life-threatening behavior
On March 24, 2015, a Germanwings aircraft crashed in the Alps. The suicidal copilot killed himself and 150 others. Pilot suicide is rare, but does happen. This research analyzed the National Transportation Safety Board's accident database (eADMS) loo...

Machine-Learning-Based Prediction of Suicide Risk Using Preliminary Questionnaire and Consultation Text.

Studies in health technology and informatics
In Japan, chat-based mental health counseling services have low response rates due to understaffing. In this article, machine learning (ML) based suicide risk classification methods are proposed. A dataset was constructed including a medical question...