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: 10401836
• 负责人: Patricia A. Arean
• 金额: $ 89.9万
• 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/10401836
• 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; Persons; 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; gradient boosting; 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.

摘要。几十年来，针对预防和早期发现自杀风险的研究在很大程度上取得了成功