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Farzin Bagheri Sheykhangafshe1 
, Khazar Tajbakhsh2 , Vahid Savabi Niri3 , Fatemeh Ramezanpour M Ermeti4 , Seyedeh Atousa Afsarpour5
, Khazar Tajbakhsh2 , Vahid Savabi Niri3 , Fatemeh Ramezanpour M Ermeti4 , Seyedeh Atousa Afsarpour5
1- Ph.D., Department of Psychology, Faculty of Humanities, Tarbiat Modares University, Tehran, Iran , farzinbagheri73@gmail.com
2- Master's Student, Department of Psychology, University of Milano-Bicocca, Milan, Italy
3- Masters, Department of Clinical Psychology, Faculty of Humanities, Islamic Azad University, Ardabil Branch, Ardabil, Iran
4- Master's, Department of Clinical Psychology, Faculty of Humanities, Islamic Azad University, Sari Branch, Sari, Iran
5- Master's, Department of Positive Islamic Psychology, Payame Noor University, Behshahr Branch, Behshahr, Iran
2- Master's Student, Department of Psychology, University of Milano-Bicocca, Milan, Italy
3- Masters, Department of Clinical Psychology, Faculty of Humanities, Islamic Azad University, Ardabil Branch, Ardabil, Iran
4- Master's, Department of Clinical Psychology, Faculty of Humanities, Islamic Azad University, Sari Branch, Sari, Iran
5- Master's, Department of Positive Islamic Psychology, Payame Noor University, Behshahr Branch, Behshahr, Iran
Abstract: (67 Views)
Objectives: Given the rapid growth of digital data and computational tools in mental health, there is an increasing need to systematically review the current evidence on the use of advanced technologies. This review aimed to examine the evolution of suicide risk identification and prevention through the application of artificial intelligence (AI) and machine learning (ML) algorithms.
Methods: A systematic review was conducted by retrieving relevant articles from Google Scholar, PubMed, ProQuest, EMBASE, PsycINFO, Web of Science, CINAHL, and Scopus, covering the period from January 2014 to September 2025. In total, 751 articles published in English were identified according to the predefined inclusion criteria and were reported in accordance with the PRISMA guidelines. Following a comprehensive quality appraisal using a standardized checklist, 25 studies with the highest methodological quality were selected for final inclusion and detailed analysis.
Results: The studies utilized diverse data sources, including text messages, social media platforms, electronic health records, and psychiatric clinical notes, with sample sizes ranging from a few hundred to several million records. AI and ML approaches applied included logistic regression, random forest, decision trees, XGBoost, and deep neural networks, with prediction accuracies ranging from 65% to over 95%. Key risk factors identified across studies included psychiatric disorders, depression, method of attempt, marital status, low education, unemployment, and social isolation. Random forest and deep neural networks consistently demonstrated the highest predictive performance.
Conclusion: AI and ML algorithms can detect complex patterns of suicide risk in both clinical and social media data, enabling early prediction and timely intervention. Random forest and deep neural network models provided the highest accuracy and practical tools for designing effective preventive strategies.
Type of Study: Rewie |
Subject:
Psychiatry and Psychology
Received: 2025/08/30 | Accepted: 2026/06/10
Received: 2025/08/30 | Accepted: 2026/06/10
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