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Suicide

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Exploring new scientific innovations in combating suicide: a stress detection wristband.

The Pan African medical journal
There is a silent pandemic of suicides around the world, with an exponential increase in suicidality and chronic suicidal ideations. The exact global estimates cannot be accurately ascertained, but analysis will put it at more than a million annually...

Reasoning language models for more transparent prediction of suicide risk.

BMJ mental health
BACKGROUND: We previously demonstrated that a large language model could estimate suicide risk using hospital discharge notes.

Exploratory Analysis of Nationwide Japanese Patient Safety Reports on Suicide and Suicide Attempts Among Inpatients With Cancer Using Large Language Models.

Psycho-oncology
OBJECTIVE: Patients with cancer have a high risk of suicide. However, evidence-based preventive measures remain unclear. This study aimed to investigate suicide prevention strategies for hospitalized patients with cancer by analyzing nationwide patie...

Data Quality Matters: Suicide Intention Detection on Social Media Posts Using RoBERTa-CNN.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Suicide remains a pressing global health concern, necessitating innovative approaches for early detection and intervention. This paper focuses on identifying suicidal intentions in posts from the SuicideWatch subreddit by proposing a novel deep-learn...

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

Scientific reports
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 of...

Suicide risk prediction for Korean adolescents based on machine learning.

Scientific reports
Traditional clinical risk assessment tools proved inadequate for reliably identifying individuals at high risk for suicidal behavior. As a result, machine learning (ML) techniques have become progressively incorporated into psychiatric care. This stu...

Decoding vital variables in predicting different phases of suicide among young adults with childhood sexual abuse: a machine learning approach.

Translational psychiatry
Young adults with childhood sexual abuse (CSA) are an especially vulnerable group to suicide. Suicide encompasses different phases, but for CSA survivors the salient factors precipitating suicide are rarely studied. In this study, from a progressive ...

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

Journal of affective disorders
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 ri...

The Application of AI to Ecological Momentary Assessment Data in Suicide Research: Systematic Review.

Journal of medical Internet research
BACKGROUND: Ecological momentary assessment (EMA) captures dynamic processes suitable to the study of suicidal ideation and behaviors. Artificial intelligence (AI) has increasingly been applied to EMA data in the study of suicidal processes.

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

Journal of medical Internet research
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 cons...