PDF(Pubmed)
Abstract：
UNASSIGNED: Recent advancements in Artificial Intelligence (AI) contributed significantly to suicide assessment, however, our theoretical understanding of this complex behavior is still limited.
UNASSIGNED: This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: This study aimed to harness AI methodologies to uncover hidden risk factors that trigger or aggravate suicide behaviors.
UNASSIGNED: 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.
UNASSIGNED: 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.
UNASSIGNED: 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.
摘要：
■人工智能（AI）的最新进展极大地促进了自杀评估，然而,我们对这种复杂行为的理论理解仍然有限。
■这项研究旨在利用人工智能方法来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228,052个Facebook帖子，该帖子由1,006个用户完成了黄金标准的哥伦比亚自杀严重程度等级量表。使用自下而上的研究管道对该数据集进行了分析，而没有先验假设，并使用新数据集的自上而下的分析对其发现进行了验证。这个次要数据集包括1062名参与者对相同自杀量表以及经过验证的测量抑郁和无聊量表的反应。
■几乎完全自动化，人工智能指导的研究管道产生了四个预测自杀风险的Facebook主题，其中最强的预测因素是无聊。使用APAPsycInfo进行的全面文献综述显示，无聊很少被视为自杀的独特风险因素。辅助数据集的自上而下的路径分析发现了无聊和自杀之间的间接关系，这是由抑郁症介导的。在主要的Facebook数据集中也观察到了等效的中介关系。然而,在这里，研究人员还观察到无聊和自杀风险之间存在直接关系.
■整合AI方法可以发现一个研究不足的自杀风险因素。这项研究表明，无聊是一种适应不良的“成分”，可能会引发自杀行为，不管抑郁症。建议进行进一步的研究，以指导临床医生注意这种负担，有时是存在的经验。
■这项研究旨在利用人工智能方法来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228,052个Facebook帖子，该帖子由1,006个用户完成了黄金标准的哥伦比亚自杀严重程度等级量表。使用自下而上的研究管道对该数据集进行了分析，而没有先验假设，并使用新数据集的自上而下的分析对其发现进行了验证。这个次要数据集包括1062名参与者对相同自杀量表以及经过验证的测量抑郁和无聊量表的反应。
■几乎完全自动化，人工智能指导的研究管道产生了四个预测自杀风险的Facebook主题，其中最强的预测因素是无聊。使用APAPsycInfo进行的全面文献综述显示，无聊很少被视为自杀的独特风险因素。辅助数据集的自上而下的路径分析发现了无聊和自杀之间的间接关系，这是由抑郁症介导的。在主要的Facebook数据集中也观察到了等效的中介关系。然而,在这里，研究人员还观察到无聊和自杀风险之间存在直接关系.
■整合AI方法可以发现一个研究不足的自杀风险因素。这项研究表明，无聊是一种适应不良的“成分”，可能会引发自杀行为，不管抑郁症。建议进行进一步的研究，以指导临床医生注意这种负担，有时是存在的经验。
