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: 10021724
• 负责人: GLENN N SAXE
• 金额: $ 61.35万
• 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 2023-05-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10021724
• 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; Machine Learning; Measures; Mental Depression; Methodology; Methods; Modeling; Observational Study; Outcome; Parents; Performance; Population; Population Heterogeneity; Post-Traumatic Stress Disorders; Predictive Factor; Prevention; Preventive Intervention; 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; novel; pediatric trauma; personalized predictions; predictive modeling; preservation; prevent; prospective; 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.

项目摘要/摘要 至少有40％的儿童会遇到创伤事件。在经历创伤的人中，15-40％的人会 发展创伤后应激障碍（PTSD）以及其他不良精神病，健康和功能结果 （此处称为儿童创伤压力-CTS）。尽管对CTS的危险因素进行了数十年的研究，但该领域有 未达到特定的风险因素模型，可以准确预测CTS结果的可能性或识别 如果发生变化，则会改变其可能性。了解导致因素的变化 根据定义，结果的变化是因果关系。关于CTS风险的文献中的绝大多数发现 无法提供这种因果知识，因为这些发现是基于相关的应用 观察者数据的方法。实验研究不能用于所有实际目的 人类有关CTS风险的研究。那是该领域的相关观察研究，作为近乎 关于CTS风险的经验知识的独家生成者，此类知识不适合指导 必须采取的行动（即干预措施）才能改变儿童获得CTS结果的可能性。我们 通过应用可以实现自信催化的方法来解决这一巨大进步障碍的建议 与大型观察数据集的推断，其中包含CTS风险变量的广泛多样性。机器 学习（ML）预测和因果建模方法将应用于发现之间的因果关系 从观察数据中测量的变量以及从这种确定的因果关系来估计 当因果变量被操纵时（即干预模拟）时，对CTS结果的影响。我们将建造 文献中与儿童创伤相关的结局模型，这需要大量伯恩 儿童的福祉，功能和发展：PTSD，抑郁，药物滥用，健康和 教育表现。