Lissak Shir, Ophir Yaakov, Tikochinski Refael, Brunstein Klomek Anat, Sisso Itay, Fruchter Eyal, Reichart Roi
The Faculty of Data and Decision Sciences, Technion - Israel Institute of Technology, Haifa, Israel.
The Centre for Human-Inspired Artificial Intelligence (CHIA), University of Cambridge, Cambridge, United Kingdom.
Front Psychiatry. 2024 May 3;15:1328122. doi: 10.3389/fpsyt.2024.1328122. eCollection 2024.
Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited.
This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors.
The primary dataset included 228,052 Facebook postings by 1,006 users who completed the gold-standard Columbia Suicide Severity Rating Scale. This dataset was analyzed using a bottom-up research pipeline without a-priory hypotheses and its findings were validated using a top-down analysis of a new dataset. This secondary dataset included responses by 1,062 participants to the same suicide scale as well as to well-validated scales measuring depression and boredom.
An almost fully automated, AI-guided research pipeline resulted in four Facebook topics that predicted the risk of suicide, of which the strongest predictor was boredom. A comprehensive literature review using revealed that boredom is rarely perceived as a unique risk factor of suicide. A complementing top-down path analysis of the secondary dataset uncovered an indirect relationship between boredom and suicide, which was mediated by depression. An equivalent mediated relationship was observed in the primary Facebook dataset as well. However, here, a direct relationship between boredom and suicide risk was also observed.
Integrating AI methods allowed the discovery of an under-researched risk factor of suicide. The study signals boredom as a maladaptive 'ingredient' that might trigger suicide behaviors, regardless of depression. Further studies are recommended to direct clinicians' attention to this burdening, and sometimes existential experience.
人工智能（AI）的最新进展对自杀评估有重大贡献，然而，我们对这种复杂行为的理论理解仍然有限。
本研究旨在利用人工智能方法揭示引发或加剧自杀行为的隐藏风险因素。
主要数据集包括1006名完成金标准哥伦比亚自杀严重程度评定量表的用户发布的228,052条脸书帖子。使用自下而上的研究流程对该数据集进行分析，无需先验假设，并使用新数据集的自上而下分析对其结果进行验证。该二级数据集包括1062名参与者对相同自杀量表以及经过充分验证的测量抑郁和无聊程度量表的回答。
一个几乎完全自动化的、由人工智能引导的研究流程得出了四个预测自杀风险的脸书话题，其中最强的预测因素是无聊。一项全面的文献综述显示，无聊很少被视为自杀的独特风险因素。对二级数据集进行的补充性自上而下路径分析揭示了无聊与自杀之间的间接关系，这种关系由抑郁介导。在主要的脸书数据集中也观察到了类似的介导关系。然而，在这里，还观察到了无聊与自杀风险之间的直接关系。
整合人工智能方法有助于发现一个研究不足的自杀风险因素。该研究表明无聊是一种可能引发自杀行为的适应不良的“因素”，无论是否存在抑郁。建议进一步开展研究，引导临床医生关注这种令人负担沉重、有时关乎生存的体验。
