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

项目总结/摘要 因为自杀行为（即，自杀和自杀企图）在很大程度上导致了全球性疾病， 财政负担，国家预防自杀行动联盟，世界卫生组织， 著名的研究人员呼吁有必要开发和改进自杀的预测模型。 行为与医疗保健专业人员接触后的时期是一个特别高的风险时期，这表明 接触后短期内自杀行为的预测（即，在大多数情况下， 研究）将解决实地援助干预努力的迫切需要。不过，此前短期 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 自我报告措施或电子健康记录，以及频繁检查双变量关联 只有一个预测因子和自杀行为之间的关系本提案的总体目标是利用 几种算法，并评估其在预测短期自杀行为后的相对性能 使用无与伦比的数据集与门诊专家（定义为1、6和12个月内）联系。我会 使用的数据来自一个前瞻性的，大规模的登记册的所有门诊心理健康专家访问的青年， 瑞典斯德哥尔摩县，由目前的奖项开始约160，000访问。 这些个人可以与评估广泛信息的人口登记册相联系（例如， 医疗问题，学术信息，邻里因素，父母的精神病理学），这是一个 显著优于先前文献，主要研究人口统计学和精神病学预测因素。 中心假设是，在一个大样本中检查众多的预测因子， 先进的统计方法将大大改善以前的自杀行为预测， 仍然略高于偶然性。为达致整体目标，本建议旨在 机器学习算法通过两个特定的目标：目标1：应用变量选择算法， 确定有限数量的突出预测因子，从而最大限度地提高可解释性;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。的 目前的提案将通过使用机器学习检查短期风险，为该领域做出重大贡献 在一个年轻的门诊样本中，包括不同的随访窗口和预测因素， 域.最后，当前提案的结果将产生积极影响，因为它将为以下两个方面提供信息：1）基本的 通过基于众多预测因素确定风险亚组进行研究，以及2）创建一个 预测工具，这将有助于临床实践。

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.