AIMC Topic: Suicide Prevention

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Applications of Large Language Models in the Field of Suicide Prevention: Scoping Review.

Journal of medical Internet research
BACKGROUND: Prevention of suicide is a global health priority. Approximately 800,000 individuals die by suicide yearly, and for every suicide death, there are another 20 estimated suicide attempts. Large language models (LLMs) hold the potential to e...

Evaluating of BERT-based and Large Language Mod for Suicide Detection, Prevention, and Risk Assessment: A Systematic Review.

Journal of medical systems
Suicide constitutes a public health issue of major concern. Ongoing progress in the field of artificial intelligence, particularly in the domain of large language models, has played a significant role in the detection, risk assessment, and prevention...

Real-time assistance in suicide prevention helplines using a deep learning-based recommender system: A randomized controlled trial.

International journal of medical informatics
OBJECTIVE: To evaluate the effectiveness and usability of an AI-assisted tool in providing real-time assistance to counselors during suicide prevention helpline conversations.

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...

Speech based suicide risk recognition for crisis intervention hotlines using explainable multi-task learning.

Journal of affective disorders
BACKGROUND: Crisis Intervention Hotline can effectively reduce suicide risk, but suffer from low connectivity rates and untimely crisis response. By integrating speech signals and deep learning to assist in crisis assessment, it is expected to enhanc...

The Most Effective Interventions for Classification Model Development to Predict Chat Outcomes Based on the Conversation Content in Online Suicide Prevention Chats: Machine Learning Approach.

JMIR mental health
BACKGROUND: For the provision of optimal care in a suicide prevention helpline, it is important to know what contributes to positive or negative effects on help seekers. Helplines can often be contacted through text-based chat services, which produce...

Machine Learning-Based Evaluation of Suicide Risk Assessment in Crisis Counseling Calls.

Psychiatric services (Washington, D.C.)
OBJECTIVE: Counselor assessment of suicide risk is one key component of crisis counseling, and standards require risk assessment in every crisis counseling conversation. Efforts to increase risk assessment frequency are limited by quality improvement...

Insights Derived From Text-Based Digital Media, in Relation to Mental Health and Suicide Prevention, Using Data Analysis and Machine Learning: Systematic Review.

JMIR mental health
BACKGROUND: Text-based digital media platforms have revolutionized communication and information sharing, providing valuable access to knowledge and understanding in the fields of mental health and suicide prevention.

A machine learning approach to detect potentially harmful and protective suicide-related content in broadcast media.

PloS one
Suicide-related media content has preventive or harmful effects depending on the specific content. Proactive media screening for suicide prevention is hampered by the scarcity of machine learning approaches to detect specific characteristics in news ...