Artificial intelligence and machine learning techniques for suicide prediction: Integrating dietary patterns and...

Area of Science:

• Digital Health
• Computational Psychiatry
• Public Health Informatics

Background:

• Suicide is a critical global health issue, causing nearly 800,000 deaths annually, particularly impacting young adults.
• There is an urgent need for innovative suicide prevention strategies beyond conventional approaches.

Purpose of the Study:

• To explore the role of artificial intelligence (AI) and machine learning (ML) in suicide risk prediction and prevention.
• To examine how AI/ML integrate complex risk factors (psychiatric, socio-economic, dietary, environmental) for enhanced interventions.

Main Methods:

• Comprehensive literature review of AI/ML studies in suicide prevention using PubMed and Web of Science.
• Assessment of AI/ML models (e.g., Random Forest, neural networks) analyzing electronic health records, social media, and digital behaviors.
• Evaluation of dietary factors and environmental contaminants (lithium, arsenic, mercury, etc.) in relation to suicidal behaviors.

Main Results:

• AI/ML models demonstrate high predictive accuracy (nearly 90%) by integrating diverse data sources.
• AI/ML approaches offer potential for geographically and demographically tailored public health interventions.
• AI-driven tools like virtual therapists and chatbots can provide immediate support, especially in underserved areas.

Conclusions:

• AI and ML are transforming suicide prevention, necessitating comprehensive models that address multifactorial risks.
• Ethical considerations and privacy concerns must be addressed for responsible AI deployment in mental health.
• AI-powered tools are crucial for immediate mental health support and enhancing suicide prevention strategies globally.