Computational Models for the Prediction and Prevention of Child Traumatic Stress - Resubmission - 1

预测和预防儿童创伤应激的计算模型 - 重新提交 - 1

• 批准号: 10455072
• 负责人: GLENN N SAXE
• 金额: $ 62.11万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-20 至 2024-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10455072
• 关键词: 18 year old; Address; Adolescent; Area; Big Data; Birth; Child; Child Traumatic Stress; Child Welfare; Clinical; Cognitive; Computer Models; Computing Methodologies; Data; Data Collection; Data Set; Decision Trees; Dependence; Development; Emotional; Ethics; Etiology; Genotype; Health; Human; Intervention; Knowledge; Learning; Left; Literature; Longitudinal Studies; Measures; Mental Depression; Methodology; Methods; Modeling; Observational Study; Outcome; Parents; Performance; Population; Population Heterogeneity; Post-Traumatic Stress Disorders; Predictive Factor; Prevention; Primary Prevention; Randomized; Reporting; Research; Retrospective Studies; Risk; Risk Factors; Secondary Prevention; Substance abuse problem; Time; Trauma; Youth; base; causal model; cohort; computerized; design; diverse data; experience; experimental study; functional outcomes; improved; innovation; machine learning prediction; novel; pediatric trauma; personalized predictions; predictive modeling; preservation; prevent; preventive intervention; prospective; risk prediction; simulation; social; tool; trauma exposure; traumatic event; traumatic stress; traumatized children

Project Summary/Abstract At least 40% of children will experience a traumatic event. Of those who experience a trauma, 15-40% will develop Posttraumatic Stress Disorder (PTSD), and other adverse psychiatric, health, and functional outcomes (herein called Child Traumatic Stress - CTS). Despite decades of research on risk factors for CTS, the field has not arrived at specific risk factor models that can accurately predict the likelihood of CTS outcomes or identify factors that – if changed – would change their likelihood. Knowledge about changes in factors that result in changes in outcomes is, by definition, causal. The vast majority of findings in the literature on risk for CTS cannot provide such causal knowledge because such findings were based on the application of correlational methods to observational data. Experimental research cannot – for all practical purposes - be conducted for human research on risk for CTS. Thus, the field is left with correlational observational research as the near exclusive generator of empirical knowledge on risk for CTS, and such knowledge is unsuitable to guide the actions (i.e. interventions) that must be taken to change children's likelihood of acquiring CTS outcomes. We propose to address this considerable barrier to progress by applying methods that can enable confident causal inference with large observational data sets containing a broad diversity of risk variables for CTS. Machine Learning (ML) predictive and causal modeling methods will be applied to discover causal relationships among measured variables from observational data: and from such determined causal relationships, to estimate the effect on a CTS outcome when a causal variable is manipulated (i.e. intervention simulation). We will build models for outcomes associated with childhood trauma in the literature and that entail significant burden to children's well-being, functioning, and development: PTSD, Depression, Substance Abuse, Health, and Educational Performance.

项目概要/摘要 至少 40% 的儿童会经历过创伤事件。在经历过创伤的人中，15-40% 的人会 出现创伤后应激障碍 (PTSD) 以及其他不良精神、健康和功能结果 （本文称为儿童创伤性应激 - CTS）。尽管对 CTS 风险因素进行了数十年的研究，但该领域 尚未建立能够准确预测 CTS 结果可能性或识别的特定风险因素模型 这些因素——如果改变——就会改变它们的可能性。了解导致以下情况发生的因素的变化 根据定义，结果的变化是因果关系。文献中绝大多数关于 CTS 风险的研究结果 无法提供此类因果知识，因为此类发现是基于相关性的应用 观测数据的方法。出于所有实际目的，实验研究不能用于 关于 CTS 风险的人类研究。因此，该领域只剩下相关观察研究作为近 CTS 风险经验知识的独家生成者，此类知识不适合指导 为改变儿童获得 CTS 结果的可能性而必须采取的行动（即干预措施）。我们 建议通过应用能够实现可靠因果关系的方法来解决这一巨大的进步障碍 利用包含广泛多样性的 CTS 风险变量的大型观测数据集进行推断。机器 将应用学习（ML）预测和因果建模方法来发现之间的因果关系 从观测数据中测量变量：并根据这种确定的因果关系来估计 操纵因果变量（即干预模拟）时对 CTS 结果的影响。我们将建设 文献中与童年创伤相关的结果模型，这些模型给儿童带来了沉重的负担 儿童的福祉、功能和发展：创伤后应激障碍（PTSD）、抑郁症、药物滥用、健康和 教育表现。