Harnessing Computerized Adaptive Testing, Transdiagnostic Theories of Suicidal Behavior, and Machine Learning to Advance the Emergent Assessment of Suicidal Youth (EASY).

利用计算机自适应测试、自杀行为跨诊断理论和机器学习来推进自杀青少年的紧急评估（EASY）。

• 批准号: 9910447
• 负责人: David A. Brent
• 金额: $ 72.16万
• 依托单位: UNIVERSITY OF PITTSBURGH AT PITTSBURGH
• 依托单位国家: 美国
• 财政年份: 2013
• 资助国家: 美国
• 起止时间: 2013-05-27 至 2023-03-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9910447
• 关键词: Accident and Emergency department; Acute; Address; Adolescent; Adult; Anxiety; Area Under Curve; Attention deficit hyperactivity disorder; Automobile Driving; Bipolar Disorder; Child; Clinic; Clinical; Clinical Research; Clinical assessments; Computing Methodologies; Conduct Disorder; Data; Development; Diagnosis; Diagnostic; Dimensions; Disease; Electronic Health Record; Emergency Situation; Emergency department visit; Evaluation; Feeling suicidal; Future; Glean; Healthcare; Hospitalization; Individual; Institutes; Intake; Intervention; Judgment; Language; Learning; Literature; Machine Learning; Measures; Mental Depression; Mental Health; Meta-Analysis; Methods; Modernization; Natural Language Processing; Oppositional Defiant Disorder; Pain; Parents; Participant; Patient Self-Report; Patients; Pennsylvania; Positioning Attribute; Positive Valence; Psyche structure; Psychometrics; Psychopathology; Reporting; Research; Resources; Risk; Risk Assessment; Risk Factors; Role; Sample Size; Sampling; Severities; Social support; Suicide; Suicide attempt; Symptoms; Telephone; Testing; Time; Youth; adolescent suicide; aged; base; child depression; computerized; follow-up; high risk; high risk population; improved; innovation; novel; predictive test; prospective; prospective test; psychiatric emergency; recruit; success; suicidal; suicidal adolescent; suicidal behavior; suicidal risk; theories; tool; trend

Abstract: The emergency assessment of acute suicidal risk in adolescents is a daunting clinical challenge because our current ability to predict suicide attempts is weak, and because the risk for suicide attempts in suicidal adolescents is high. Nevertheless, there have been no studies that have examined the best approaches to the prediction of suicidal behavior in suicidal youth presenting to a psychiatric emergency department (PED). To address this research gap, we propose a study of 1800 youth presented to a regional PED, 1350 of whom present for evaluation of suicidal risk, in which youth are assessed in the PED, and followed up at 1, 3, and 6 months to determine which youth have made a suicide attempt. We propose 3 complementary approaches to assessment of suicidal risk. First, in this competitive renewal, we build on our success in developing computerized adaptive tests for 6 diagnostic groups, plus suicidal risk, during our previous project period. These self- and parent-reports can be completed in a total of 10-15 minutes. Second, because theory-driven assessments of suicide risk have strong predictive power in adults, but have never been tested prospectively in adolescents, we propose to test the predictive power of measures of Shneidman’s psychache (mental pain) and Joiner’s Interpersonal Theory of Suicide, which posits interactive roles of perceived burdensomeness, thwarted belonging, and acquired capacity for suicide in driving suicidal risk. Third, we aim to use machine learning (ML) and natural language processing (NLP) of electronic health records (EHRs) to identify youth at risk for suicide attempts. We hypothesize that each of these approaches: (1) CATs for suicide risk and for depression, anxiety, bipolar, ADHD, oppositional defiant, and conduct disorders); (2) theory-derived measures of suicidal risk; and (3) ML and NLP of EHRs, will each be superior to clinical assessment alone in the prediction of attempts, and that the combination of the 3 approaches will be more powerful than any one of these approaches alone. This study is innovative because it is one of the first to use CATs for the prediction of suicidal risk, in a consistently high risk population, the first prospective test of two leading theories of suicide in adolescents, the first to use machine learning and natural language processing to identify EHR predictors of suicide attempts in adolescents, and the first to test a combination of approaches to the identification of imminent suicidal risk in adolescents in a sufficiently large, high risk sample. The study is of potentially high impact because it could identify brief, easily disseminated assessment strategies to identify youth at high risk for suicidal behavior and add to clinicians’ ability to match intensity and type of resources to those at greatest clinical need. The approaches to be tested in this study could yield assessments that reflect the two imperatives of emergency mental health care: brevity and accuracy. With better ability to identify who is at risk for suicidal behavior, we will be in a much stronger position to identify who needs intervention and reverse the disturbing, decade-long trend of increases in adolescent suicide and suicidal behavior.

摘要： 青少年急性自杀风险的紧急评估是一项艰巨的临床挑战，因为我们的 目前预测自杀企图的能力很弱，而且由于自杀倾向的风险很高， 青少年高。然而，还没有任何研究审查了最好的办法， 在精神病急诊科（PED）就诊的自杀青年中预测自杀行为。到 为了解决这一研究空白，我们建议对1800名青年进行一项研究，提交给一个地区PED，其中1350人， 存在自杀风险评估，其中青年在PED中进行评估，并在1、3和6时进行随访 几个月来确定哪些年轻人有自杀企图。我们提出了三种互补的方法， 评估自杀风险。首先，在这次充满竞争的更新中，我们在发展 在我们之前的项目期间，对6个诊断组进行了计算机化适应性测试，加上自杀风险。 这些自我报告和家长报告可以在10 - 15分钟内完成。第二，因为理论驱动 自杀风险评估在成年人中具有很强的预测能力，但从未在成人中进行过前瞻性测试。 青少年，我们建议测试预测能力的措施，Shneidman的心理疼痛（精神痛苦） 以及乔伊纳的自杀的人际关系理论，该理论假定了感知负担的互动作用， 归属感受挫，以及获得自杀能力，从而导致自杀风险。第三，我们的目标是使用机器 学习（ML）和自然语言处理（NLP）的电子健康记录（EHR），以确定青年在 自杀企图的风险。我们假设这些方法中的每一种：（1）自杀风险和 抑郁、焦虑、双相、ADHD、对立违抗和行为障碍）;（2）理论衍生的测量 （3）EHR的ML和NLP，在以下方面均优于单独的临床评估： 预测的尝试，这3种方法的组合将比任何一个更强大， 这些方法本身。这项研究是创新的，因为它是第一个使用CAT预测 自杀风险，在一个一贯的高风险人群中，第一次前瞻性测试的两个主要理论的自杀， 青少年，第一个使用机器学习和自然语言处理来识别EHR预测因子的人。 青少年自杀企图，并首次测试的方法相结合，以确定 在足够大的高风险样本中，青少年的迫在眉睫的自杀风险。这项研究具有潜在的高 影响，因为它可以确定简单、易于传播的评估战略，以确定高风险青年 并增加临床医生的能力，以匹配强度和类型的资源，以那些在最大的 临床需要本研究中测试的方法可以产生反映这两个方面的评估。 紧急精神卫生保健的必要性：简洁和准确。更好地识别谁处于风险中 对于自杀行为，我们将处于更有利的地位，以确定谁需要干预，并扭转 令人不安的是，青少年自杀和自杀行为的增长趋势长达十年。