Using Search Engine Data for Detection and Early Intervention in Suicide Prevention

抽象的。几十年来为改善自杀风险的预防和早期发现而进行的研究在很大程度上 结果发现了谁最有可能考虑自杀，但不是最有可能考虑自杀的时间或是否 会发生的。大多数检测方法都假设患者与医疗保健系统有接触，这只是 覆盖一部分高危人群。许多有自杀风险的人不寻求专业人士 由于缺乏时间、耻辱和对他们在医疗保健系统中将如何治疗的恐惧而提供帮助。它是 当务之急是，我们必须开发出不依赖于系统的方法来识别自杀的近端风险- 水平接触。基于网络的搜索工具无处不在，46%的全球人口使用互联网进行搜索 全球每年的信息搜索和1.2万亿次搜索。根据我们的初步数据，我们建议 这种在线搜索引擎行为可能被证明是一种有效的、私密的和直接的方法 任何人的近端自杀风险检测，无论他们与护理系统的接触如何。我们将招募 1,000名患有精神疾病的人有不同的自杀风险。参与者将为我们提供访问谷歌的权限 外卖(GTO)数据，包括搜索引擎历史和行为，包括YouTube。参与者将 包括在过去一年中报告自杀未遂人数(N=500)、自杀未遂人数 一年多前(N=250)，以及有过自杀念头但从未尝试过的(N=250)。所有人都会 使用黄金标准的自杀行为研究工具参与进来。使用案例交叉设计，我们将 评估间歇性暴露(基于近端风险因素的搜索)，并立即产生短暂影响 风险和突如其来的结果(自杀未遂)。案例交叉设计是一个经过充分测试和验证的设计 尤其是在瞬时事件可能触发诸如心血管事件之类的急性事件的情况下， 由于环境暴露造成的伤害和死亡，已经通过访谈数据进行了研究，以确定 自杀未遂的警示标志。为了进一步预测自杀企图/S，我们将使用基于稳健集成的 机器学习方法，如随机森林，梯度提升，以评估预测性质 定性和定量特征。这项研究将在协作传播规划过程中结束 与我们的社区合作伙伴一起。因此，这项回顾和前瞻性研究将GTO数据与 仔细评估自杀的想法和行为有可能在搜索和搜索中识别警告信号 YouTube数据预测何时有自杀风险，并为创新自杀途径奠定基础 预防。

ABSTRACT. Decades of research to improve the prevention and early detection of suicide risk has largely resulted in the detection of who is most likely to consider suicide, but not when or if that is most likely to happen. Most detection methods presume patients are in contact with the healthcare system, which only reaches a proportion of the at-risk population. Many people at high risk for suicide do not seek professional help because of lack of time, stigma, and fear regarding how they will be treated in the health care system. It is imperative that we develop methods that can identify proximal risk for suicide that does not depend on system- level contact. Web-based search tools are ubiquitous, with 46% of the global population using the internet for information searches and 1.2 trillion searches per year worldwide. Based on our preliminary data, we propose that this online search-engine behavior may prove to be an effective, private, and immediate method of proximal risk detection of suicide for anyone, regardless of their contact with systems of care. We will recruit 1,000 people with mental illness with varying risk for suicide. Participants will provide us access to Google Take-Out (GTO) data, which includes search-engine history and behavior including YouTube. Participants will include those who have report a suicide attempt in the past year (N=500), those who have made an attempt over a year ago (N=250), and those who have thoughts of suicide but never attempted (N=250). All will participate using gold-standard suicide behavior research instruments. Using a case-crossover design, we will evaluate the intermittent exposures (search based proximal risk factors) with an immediate and transient effect on risk and an abrupt outcome (suicide attempt). The case-crossover design is a well-tested and proven approach especially in cases where transient events can trigger acute events such as cardiovascular events, injuries, and death due to environmental exposures and has been studied with interview data to determine warning signs for suicide attempts. Further for predicting suicidal attempt/s, we will use robust ensemble-based machine learning methods such as random forest, gradient boosting to evaluate the predictive nature of qualitative and quantitative features. The study will conclude in a collaborative dissemination planning process with our community partners. Thus, this retrospective and prospective study that aligns GTO data with carefully assessed suicidal thoughts and behaviors has the potential to identify warning signs in search and YouTube data that predict when suicidal risk and lay the groundwork for innovative pathways to suicide prevention.