Automated Detection of Suicide Attempt Risk among Bereaved Individuals

自动检测丧亲者的自杀未遂风险

• 批准号: 9885577
• 负责人: Paul K Maciejewski
• 金额: $ 24.81万
• 依托单位: WEILL MEDICAL COLL OF CORNELL UNIV
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9885577
• 关键词: Behavioral; Bereavement; Caring; Cessation of life; Clinical; Communities; Data; Data Collection; Data Set; Detection; Development; Diagnostic; Disease; Dreams; Feeling; Feeling suicidal; Future; Grief reaction; Healthcare; Home environment; Implicit Association Test; Individual; Life; Linguistics; Machine Learning; Measures; Memory; Mental Health; Modeling; Natural Language Processing; New York; Patient Self-Report; Patients; Persons; Reaction; Reporting; Research; Resources; Risk; Sampling; Savings; Severities; Subgroup; Suicide; Suicide attempt; Surveys; Techniques; Testing; Thinking; Time; Training; United Kingdom; Visit; base; end of life care; expressive writing; follow up assessment; follow-up; help-seeking behavior; informal support; loved ones; metropolitan; predictive modeling; predictive test; primary outcome; recruit; skills; suicidal behavior; suicidal individual; suicidal risk; suicide model; tool; web site; web-based tool

Project Summary Bereaved individuals, especially those who meet criteria for Prolonged Grief Disorder (PGD) and those bereaved by suicide, have substantial risk of suicidal ideation, suicide attempts, and suicide. These vulnerable bereaved subgroups are also less likely to receive informal support or access mental health care, creating a pressing need to detect their suicide risk. Each year, well over a thousand unsolicited bereaved individuals visit our Cornell Center for Research on End-of-Life Care website and complete an online tool to determine if they meet criteria for PGD (over 6,000 completers to date). Compared to community-based bereaved samples, those who complete our online tool disproportionately meet criteria for PGD (e.g., ~30% vs. ~10%) and are suicide bereaved (~10% vs. ~2%). Thus, astoundingly, >35% of those who visit our Center website and complete our online diagnostic tool are at significantly elevated risk for suicidal ideation and/or attempts. To respond to the need to detect suicidal thoughts and behaviors (STBs) among our bereaved website visitors, we propose to develop a web-based tool for the detection of suicide attempt risk. We will leverage our Living Memory Home, an online memorial application residing on our Center website, by enhancing its features to optimize data collection, including data on potential implicit indicators of a bereaved person's suicide attempt risk (Aim #1). Data will be gathered on 100 Living Memory Home users daily for a week, followed by a 1-week, 1- and 6-month post-baseline follow-up assessment. The study will generate ~30 texts/subject in the Living Memory Home's Imagined Dialogues with the deceased, and reflections, dreams, stories, and touchstones in Narrative Notes (~3,000 texts in total from all users). Boot-strapping resampling, natural language processing and machine learning techniques will then be applied to develop machine learning models predicting suicide attempt risk based on bereaved subjects' baseline, 1-week, 1- and 6-month Columbia Suicide Severity Rating Scale scores (Aim #2). Our primary outcome will be the 1-week CSSRS score. We will, thus, develop and then pilot test an automated way to detect bereaved persons'; suicide attempt risk based on their interactions with the Living Memory Home. This is a first step toward development of a safe, accurate, potentially scalable online tool for the detection of suicide attempt risk among bereaved individuals.

项目摘要 丧失亲人的人，特别是那些符合长期悲伤障碍（PGD）标准的人， 因自杀而失去亲人，有自杀意念、自杀企图和自杀的重大风险。这些弱势 失去亲人的亚群体也不太可能得到非正式的支持或获得心理健康护理， 迫切需要检测他们的自杀风险。每年，有超过一千名未经请求的丧亲者访问 我们的康奈尔大学临终关怀研究中心网站，并完成在线工具，以确定他们是否 符合PGD标准（迄今为止超过6，000名完成者）。与基于社区的丧亲样本相比， 那些完成我们在线工具的人不成比例地符合PGD的标准（例如，约30%对约10%）， 自杀（~10% vs. ~2%）。因此，令人惊讶的是，超过35%的访问我们中心网站的人， 完成我们的在线诊断工具，自杀意念和/或企图的风险显着升高。到 为了满足检测失去亲人的网站访问者的自杀想法和行为（STB）的需求，我们 建议发展一套网上工具，以侦测企图自杀的风险。我们将利用我们的生活 记忆之家，一个在线纪念应用程序驻留在我们的中心网站，通过增强其功能， 优化数据收集，包括关于丧亲者自杀企图潜在隐含指标的数据 风险（目标1）。数据将收集100生活记忆家庭用户每天一周，然后是1周， 1-和6个月基线后随访评估。该研究将在Living中生成约30篇文章/主题 记忆之家与死者的想象对话，以及反思，梦想，故事和试金石， 叙述性说明（所有用户共约3，000条文本）。自举响应，自然语言处理 机器学习技术将用于开发预测自杀的机器学习模型 基于丧亲受试者基线、1周、1个月和6个月哥伦比亚自杀严重程度评级的尝试风险 量表评分（目标#2）。我们的主要结局是1周CSSRS评分。因此，我们将发展， 试点测试一种自动化的方法来检测丧亲者的自杀企图风险的基础上，他们的互动与 生活记忆之家这是开发安全、准确、可扩展的 用于检测失去亲人的人自杀未遂风险的在线工具。