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

摘要。几十年来，为了改善自杀风险的预防和早期发现， 导致检测谁最有可能考虑自杀，但不是何时或是否最有可能 发生大多数检测方法假定患者与医疗保健系统接触， 达到一定比例的高危人群。许多自杀高风险的人不寻求专业人士 由于缺乏时间、耻辱和担心他们在医疗保健系统中将如何受到治疗，他们无法获得帮助。是 当务之急是我们开发出一种方法，可以识别出自杀的近端风险，而不依赖于系统- 水平接触。基于网络的搜索工具无处不在，全球46%的人口使用互联网 全球每年有1.2万亿次搜索。根据我们的初步数据，我们建议 这种在线搜索引擎的行为可能被证明是一种有效的，私人的，直接的方法， 对任何人来说，无论他们是否与护理系统接触，都有近端的自杀风险检测。我们将招募 1,000名有不同自杀风险的精神疾病患者。参与者将为我们提供访问Google 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有自杀企图的人（N=500）， 1年以上（N=250），有自杀念头但从未尝试过（N=250）。一切都会 使用黄金标准的自杀行为研究工具。使用病例交叉设计，我们将 评估间歇性暴露（基于近端风险因素的搜索）的即时和短暂影响 风险和突然的结果（自杀企图）。病例交叉设计是一种经过充分测试和验证的 特别是在瞬态事件可能触发心血管事件等急性事件的情况下， 伤害和死亡，由于环境暴露，并已与访谈数据进行了研究，以确定 自杀企图的警告信号。为了进一步预测自杀企图，我们将使用基于鲁棒整体的 机器学习方法，如随机森林，梯度提升，以评估的预测性质 定性和定量特征。这项研究将在一个合作传播规划过程中结束 我们的社区合作伙伴。因此，这项回顾性和前瞻性研究将GTO数据与 仔细评估自杀想法和行为有可能在搜索中识别出警告信号， YouTube数据预测自杀风险，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.