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| Exploring the Antecedents of Suicide Risk and Identifying and Predicting Suicide Risk: The Application of Machine Learning |
| 1. Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing 100875, China; 2. School of Applied Psychology, Beijing Normal University at Zhuhai, Zhuhai 519087, China |
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Abstract Exploring the antecedents of suicide risk and identifying and predicting suicide risk are challenging issues in the field of mental health. This paper summarizes the breakthroughs made by data-driven machine learning in addressing this problem as compared to theory-driven traditional methods. Machine learning utilizes advanced modeling techniques to comprehensively explore the antecedents of suicide risk and their relative importance. Moreover, it can handle complex and diverse data, enabling better longitudinal design and real-time monitoring of suicide risk. Consequently, this significantly improves the accuracy of suicide risk identification and prediction. This paper also discusses potential limitations of machine learning methods and proposes solutions and future directions, providing valuable insights for both research and practical applications.
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Published: 13 November 2023
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| Cite this article: |
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Zhang Li-ying,Wang Wen-chao,Wu Xin-chu, et al. Exploring the Antecedents of Suicide Risk and Identifying and Predicting Suicide Risk: The Application of Machine Learning[J]. 应用心理学, 2025, 31(2): 122-133.
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| URL: |
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http://www.appliedpsy.cn/EN/Y2025/V31/I2/122 |
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