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

Abstract

Background: Suicide remains a leading cause of death globally, with nearly 800,000 deaths annually, particularly among young adults in regions like Europe, Australia, and the Middle East, highlighting the urgent need for innovative intervention strategies beyond conventional methods. Objectives: This review aims to explore the transformative role of artificial intelligence (AI) and machine learning (ML) in enhancing suicide risk prediction and developing effective prevention strategies, examining how these technologies integrate complex risk factors, including psychiatric, socio-economic, dietary, and environmental influences. Methods: A comprehensive review of literature from databases such as PubMed and Web of Science was conducted, focusing on studies that utilize AI and ML technologies. The review assessed the efficacy of various models, including Random Forest, neural networks, and others, in analyzing data from electronic health records, social media, and digital behaviors. Additionally, it evaluated a broad spectrum of dietary factors and their influence on suicidal behaviors, as well as the impact of environmental contaminants like lithium, arsenic, fluoride, mercury, and organophosphorus pesticides. Conclusions: AI and ML are revolutionizing suicide prevention strategies, with models achieving nearly 90 % predictive accuracy by integrating diverse data sources. Our findings highlight the need for geographically and demographically tailored public health interventions and comprehensive AI models that address the multifactorial nature of suicide risk. However, the deployment of these technologies must address critical ethical and privacy concerns, ensuring compliance with regulations and the development of transparent, ethically guided AI systems. AI-driven tools, such as virtual therapists and chatbots, are essential for immediate support, particularly in underserved regions.

Keywords

• Artificial intelligence (AI)
• Machine learning (ML)
• Suicide prediction
• Environmental contaminants
• Dietary influences