Artificial Intelligence as a Tool to Prevent Autoaggressive Destructive Behavior Among Children and Adolescents: a Brief Overview

Saduakassova K. Zhanuzakov M. Kassenova G. Serbin V.
2024 National Scientific Medical Center

Journal of Clinical Medicine of Kazakhstan
2024 #21 Issue 6 24 - 29 pp.

Suicides and suicidal behaviors are complex disorders with diverse symptoms, often lacking clear etiology, especially in spontaneous or childhood cases. This complicates timely diagnosis, therapy, and treatment. As a result, research into markers for depression and suicidal behavior continues. The use of artificial intelligence represents a significant advancement in suicide prevention, offering new tools for early detection and intervention to improve outcomes for at-risk individuals. According to the World Health Organization (WHO), 726,000 people commit suicide, not counting the much larger number of people who attempt suicide each year. Suicides occur throughout life, but in 2021 they became one of the leading causes of death among 15-29 year-olds worldwide. This problem is also relevant in Kazakhstan, and this article is the first to reflect an interdisciplinary approach to suicide prevention among minors using AI methods in application to scientific data obtained in the study of respondents with suicidal behavior. Suicide is a significant public health issue with profound societal impacts. Its effects extend beyond the loss of life, leading to emotional suffering for families and loved ones, and economic losses from reduced productivity and increased healthcare costs. For each suicide, there are over 30 attempted suicides, compounding the social and economic burden. The repercussions affect countless individuals, both directly and indirectly, leaving long-lasting emotional and financial strain. Additionally, the economic impact includes treatment costs for psychosomatic and mental disorders in those left behind, highlighting the extensive and multifaceted consequences of suicidal behavior.

age-related ontogenesis , artificial intelligence , children and adolescents , machine learning , neural networks , risk factors , suicide prevention , young people

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Higher school of medicine, Faculty of medicine and healthcare, Al-Farabi Kazakh National University, Almaty, Kazakhstan
Department of computer science, Al-Farabi Kazakh National University, Almaty, Kazakhstan
Department of Cybersecurity, information processing and storage, Kazakh National Research Technical University, Almaty, Kazakhstan

Higher school of medicine
Department of computer science
Department of Cybersecurity

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