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：人工智能的最新进展显著促进了自杀评估，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，揭示触发或加剧自杀行为的隐藏风险因素。方法：主要数据集包含1006名完成了金标准哥伦比亚-自杀严重程度评定量表的用户的228052条Facebook发帖。该数据集通过一个无先验假设的自下而上研究流程进行分析，其发现通过一个新数据集的自上而下分析进行了验证。次要数据集包括1062名参与者对同一自杀量表以及经过验证的抑郁和无聊量表的回答。结果：一个几乎完全自动化、由人工智能引导的研究流程识别出四个预测自杀风险的Facebook主题，其中最强预测因子为无聊感。利用APA PsycInfo进行的全面文献综述表明，无聊感很少被视为自杀的独特风险因素。对次要数据集补充性的自上而下路径分析揭示了无聊感与自杀之间的间接关系，该关系由抑郁介导。在主Facebook数据集中也观察到了类似的介导关系。然而，在此数据集中，还观察到了无聊感与自杀风险之间的直接关系。结论：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。本研究提示，无聊感作为一种适应不良的“成分”，可能触发自杀行为，且与抑郁无关。建议开展进一步研究，以引导临床医生关注这种令人困扰且有时涉及存在感的体验。