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

使用搜索引擎数据进行自杀预防的检测和早期干预

• 批准号: 10207109
• 负责人: Patricia A. Arean
• 金额: $ 87.67万
• 依托单位: UNIVERSITY OF WASHINGTON
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-05-05 至 2024-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10207109
• 关键词: Acute; American; Behavior; Behavioral; Cardiovascular system; Caring; Cause of Death; Centers for Disease Control and Prevention (U.S.); Cessation of life; Characteristics; Classification; Clinical; Cohort Studies; Communities; Crossover Design; Data; Decision Making; Detection; Diagnostic Procedure; Early Diagnosis; Early Intervention; Environmental Exposure; Ethics; Event; Facebook; Family; Feeling suicidal; Foundations; Friends; Fright; Future; Gold; Healthcare; Healthcare Systems; Hour; Individual; Injury; Inpatients; Internet; Interview; Knowledge; Length; Medical; Medicine; Mental disorders; Meta-Analysis; Methods; National Institute of Mental Health; Nature; Online Systems; Outcome; Participant; Pathway interactions; Patients; Pattern; Population; Populations at Risk; Prevention; Primary Health Care; Privatization; Process; Prospective Studies; Recording of previous events; Reporting; Research; Research Personnel; Retrospective Studies; Risk; Risk Factors; Services; Standardization; Suicide; Suicide attempt; Suicide prevention; Surveys; System; Testing; Thinking; Time; Twitter; Validation; Work; base; care systems; cohort; cost; detection method; digital; follow-up; high risk; ideation; improved; information seeking behavior; innovation; instrument; machine learning method; mortality; online intervention; predictive tools; prevention service; prognostic signature; prospective; random forest; recruit; screening; search engine; social media; social stigma; suicidal behavior; suicidal morbidity; suicidal risk; tool; volunteer

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.

抽象的。数十年的研究以改善预防和早期发现自杀风险的研究很大程度上已经很大程度上了 导致发现谁最有可能考虑自杀，但不是什么时候或最有可能的 发生。大多数检测方法都认为患者与医疗系统接触，仅 达到高危人群的比例。许多有自杀风险的人不会寻求专业 由于缺乏时间，污名和对如何在医疗保健系统中的对待而提供帮助。这是 我们必须开发可以确定自杀近端风险的方法，而自杀的近端风险不依赖于系统 电平接触。基于Web的搜索工具无处不在，46％的全球人口使用Internet用于 全球信息搜索和每年1.2万亿搜索。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，私人的和直接的方法 无论他们与护理系统的接触如何，任何人都可以自杀的近端风险检测。我们将招募 1,000名精神疾病患者有自杀风险。参与者将使我们访问Google 外卖（GTO）数据，其中包括搜索引擎历史记录和行为，包括YouTube。参与者会 包括那些在过去一年中报告自杀企图的人（n = 500），那些尝试过的人 一年多以前（n = 250），以及那些有自杀念头但从未尝试过的人（n = 250）。一切都会 使用金标准的自杀行为研究工具参与。使用案例交叉设计，我们将 评估具有直接和短暂效应的间歇性暴露（基于搜索的近端风险因素） 风险和突然结果（自杀企图）。案例交叉设计是经过良好测试且经过验证的 尤其是在短暂事件可以触发急性事件（例如心血管事件）的情况下， 受伤和由于环境暴露而导致的死亡，并已通过访谈数据进行了研究以确定 自杀企图的警告信号。进一步预测自杀企图，我们将使用基于强大的集合 机器学习方法，例如随机森林，梯度提升以评估的预测性质 定性和定量特征。该研究将在协作分发计划过程中得出结论 与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 精心评估的自杀思想和行为有可能识别搜索中的警告标志 YouTube数据可以预测自杀风险何时并为自杀的创新途径奠定基础 预防。