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

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.

Exploring the Determinants of Suicidal Behavior: Conventional and Emergent Risk (DISCOVER): a feasibility study

BACKGROUND

Suicidal behavior is a growing public health concern resulting in morbidity and premature death. Although certain factors such as age, sex, and psychiatric disorders have been consistently reported to be associated with suicidal behavior, other factors including biological markers, diet, and physical activity may also influence suicidal behavior. The purpose of this pilot study was to evaluate the feasibility of conducting a full-scale study to identify the conventional and novel risk factors of suicidal behavior in individuals who made a recent suicide attempt.

METHODS

This pilot study was a case-control study of participants with recent (within 1 month of admission) suicide attempts admitted to hospital and compared to two control groups: 1) psychiatric inpatient participants without a history of suicide attempts and 2) community-based controls. We collected information on demographic variables, circumstances of suicide attempts (for cases), medical and psychiatric diagnoses, behavioral patterns, physical measurements, and social factors. Blood and urine samples were also collected for biological markers. Feasibility outcomes are as follows: 1) 50 % of all eligible cases will consent to participate, 2) 50 cases and 100 controls per year can be recruited, and 3) at least 80 % of the participants will provide blood samples for DNA and biological markers.

RESULTS

We recruited 179 participants in total; 51 cases, 57 psychiatric controls without suicide attempt, and 71 non-psychiatric controls in Hamilton, Ontario. Recruitment rate was 70 % (213/304), and we obtained urine and blood specimens from 90 % (191/213) of participants. Questionnaire completion rates were high, and data quality was very good with few data-related queries to resolve. We learned that cases tended to be hospitalized for long periods of time and the suicide attempt occurred more than a month ago in many of the cases; therefore, we expanded our inclusion criterion related to timing of suicide attempt to 3 months instead of 1 month.

CONCLUSIONS

The study procedures needed certain modifications including extending the time between suicide attempt and date of recruitment, and more detailed questionnaires related to diet were necessary while other questionnaires such as social support needed to be shortened. Overall, this study showed that it is feasible to conduct a larger-scale study.