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

项目摘要/摘要 至少40%的儿童会经历创伤性事件。在经历过创伤的人中，15%-40%的人会 出现创伤后应激障碍(PTSD)，以及其他不利的精神、健康和功能后果 (这里称为儿童创伤应激-CTS)。尽管几十年来对CTS的风险因素进行了研究，但该领域已经 没有得出可以准确预测CTS结果的可能性或确定 这些因素--如果改变--将改变它们的可能性。关于导致以下因素变化的知识 根据定义，结果的变化是因果的。关于CTS风险的文献中的绝大多数发现 不能提供这样的因果知识，因为这样的发现是基于相关性的应用 方法对观测数据进行统计分析。实验研究不能--出于所有实际目的--进行 CTS风险的人体研究。因此，该领域留下了与之相关的观测研究作为近在咫尺的工作。 CTS风险经验知识的独家生成者，这种知识不适合指导 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 建议通过应用能够确保因果关系的方法来解决这一相当大的进步障碍 使用包含CTS风险变量广泛多样性的大型观测数据集进行推断。机器 学习(ML)预测和因果建模方法将被应用于发现 从观测数据测量的变量：并从这种确定的因果关系中估计 操纵因果变量(即干预模拟)时对CTS结果的影响。我们将建造 文献中与儿童创伤相关的结局模型，这需要极大的负担 儿童的幸福、功能和发展：创伤后应激障碍、抑郁、药物滥用、健康和 教育表现。

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.