Background: Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited. Objective: This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors. Method: 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. Results: 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 APA PsycInfo 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. Conclusions: 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.

翻译：背景：近年来人工智能（AI）的进步显著推动了自杀评估领域的发展，然而我们对这一复杂行为的理论理解仍然有限。目的：本研究旨在利用人工智能方法揭示诱发或加重自杀行为的潜在风险因素。方法：主要数据集包含1,006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Facebook帖子。采用无先验假设的自下而上研究流程分析该数据集，并通过新数据集的自上而下分析验证结果。次要数据集包含1,062名参与者对该自杀量表以及经充分验证的抑郁和无聊量表的应答。结果：一个近乎全自动、AI引导的研究流程识别出四个预测自杀风险的Facebook话题，其中最强预测因子为无聊。使用APA PsycInfo进行的全面文献回顾显示，无聊很少被视为自杀的独特风险因素。对次要数据集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，该关系由抑郁中介。在主要Facebook数据集中也观察到类似的中介关系，但在此数据集中还发现了无聊与自杀风险之间的直接关系。结论：整合AI方法有助于发现一个研究不足的自杀风险因素。本研究提示，无聊作为一种适应不良的"成分"，可能独立于抑郁而诱发自杀行为。建议开展进一步研究，引导临床医生关注这种令人负担甚至关乎存在体验的体验。