Predicting Short-Term Risk for Suicidal Behavior after Contact with Outpatient Specialists: A Machine Learning Approach

与门诊专家联系后预测自杀行为的短期风险：机器学习方法

• 批准号: 9981424
• 负责人: Lauren Marie O'Reilly
• 金额: $ 4.55万
• 依托单位: TRUSTEES OF INDIANA UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-07-04 至 2022-07-03
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/9981424
• 关键词: Address; Adult; Algorithms; Award; Basic Science; Cessation of life; Clinical; Collaborations; County; Data; Data Set; Development; Electronic Health Record; Financial Hardship; Goals; Health Professional; Healthcare Systems; Individual; Inpatients; Intervention; Knowledge; Link; Literature; Logistic Regressions; Machine Learning; Measures; Medical; Mental Health; Meta-Analysis; Methods; Modeling; Neighborhoods; Outcome; Outpatients; Patient Self-Report; Performance; Psychopathology; Publishing; Research; Research Personnel; Risk; Risk Factors; Sampling; Specialist; Specific qualifier value; Statistical Methods; Subgroup; Suicide; Suicide attempt; Suicide prevention; Sweden; Techniques; Translating; Visit; World Health Organization; Youth; base; behavior prediction; burden of illness; clinical care; clinical decision-making; clinical practice; demographics; design; effective intervention; follow-up; high risk; improved; indexing; machine learning algorithm; machine learning method; population based; predictive modeling; prospective; statistics; suicidal; suicidal behavior; suicidal morbidity; suicidal risk; tool; translational pipeline

Project Summary/Abstract Because suicidal behavior (i.e., suicide and suicide attempt) significantly contributes to global disease and financial burden, the National Action Alliance for Suicide Prevention, the World Health Organization, and prominent researchers have called for the need to develop and improve prediction models for suicidal behavior. The period after contact with a health care professional is a particularly high-risk period, suggesting that prediction of suicidal behavior in the short-term after contact (i.e., defined as within one year in most studies) would address a critical need in the field to aid intervention efforts. However, previous short-term research has been limited by numerous factors, including a lack of research on youth samples, an overreliance on self-report measures or electronic health records, and the frequent examination of bivariate associations between only one predictor and suicidal behavior. The overall objective of the current proposal is to utilize several algorithms and assess their relative performance in the prediction of short-term suicidal behavior after contact with an outpatient specialist (defined as within 1, 6, and 12 months) using an unparalleled dataset. I will use data from a prospective, large-scale register of all outpatient mental health specialist visits among youth in Stockholm County, Sweden, consisting of approximately 160,000 visits by the onset of the current award. These individuals can be linked to population-based registers assessing a broad range of information (e.g., medical problems, academic information, neighborhood factors, parental psychopathology), which is a significant advantage over prior literature primarily studying demographic and psychiatric predictors in isolation. The central hypothesis is that the examination of numerous predictors within a large sample and the use of advanced statistical methods will significantly improve upon previous suicidal behavior prediction, which has remained slightly above chance. To achieve the overall objective, the current proposal is designed to apply machine learning algorithms through two specific aims: Aim 1: Apply variable selection algorithms that determine a limited number of salient predictors and, therefore, maximize interpretability; Aim 2: Apply ensemble algorithms that aggregate machine learning models and, therefore, maximize predictive power. The current proposal will significantly contribute to the field by examining short-term risk using machine learning techniques among a youth, outpatient sample, including varying follow-up windows and predictors across domains. Finally, the results from the current proposal will have positive impact by informing both 1) basic research through the identification of at-risk subgroups based on numerous predictors, and 2) the creation of a prediction tool that will aid in clinical practice.

项目摘要/摘要 因为自杀行为(即自杀和自杀企图)对全球疾病和 财政负担、全国预防自杀行动联盟、世界卫生组织和 著名研究人员呼吁有必要开发和改进自杀预测模型 行为。与卫生保健专业人员接触后的这段时间是特别高风险的时期，建议 接触后短期内自杀行为的预测(即，大多数情况下定义为一年内 研究)将解决实地援助干预工作的迫切需要。不过，之前的短期 研究受到许多因素的限制，包括缺乏对青年样本的研究，过度依赖 关于自我报告措施或电子健康记录，以及对双变量相关性的频繁检查 只有一个预测者和自杀行为之间的关系。当前提案的总体目标是利用 几种算法及其在预测自杀后短期自杀行为中的相对表现 使用无与伦比的数据集联系门诊专家(定义为1、6和12个月内)。这就做 使用来自一项前瞻性的大规模登记的数据，该登记记录了#年青年所有门诊精神卫生专家的就诊情况 瑞典斯德哥尔摩郡，截至本奖项开始时，访问人数约为160,000人。 这些个人可以链接到评估广泛信息的基于人口的登记(例如， 医疗问题、学术信息、邻里因素、父母精神病)，这是一种 与以前的文献相比有显著的优势，主要是单独研究人口统计学和精神病学预测因素。 中心假设是，在大样本中检查众多预测因素并使用 先进的统计方法将显著改善以前的自杀行为预测，后者已经 仍然略高于机会。为实现总体目标，目前的提案旨在适用 通过两个具体目标实现机器学习算法：目标1：应用变量选择算法 确定有限数量的显著预测因素，从而最大化可解释性；目标2：应用 集成算法，聚合机器学习模型，从而最大限度地提高预测能力。这个 目前的提案将通过使用机器学习来检查短期风险，从而对该领域做出重大贡献 青年门诊样本中的技术，包括不同的随访窗口和预测因素 域名。最后，当前提案的结果将产生积极的影响，因为这两个基础 通过基于众多预测因素确定风险亚组进行研究，以及2)创建 有助于临床实践的预测工具。