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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

基本信息

批准号：
10206005
负责人：
GLENN N SAXE
金额：
$ 60.43万
依托单位：
NEW YORK UNIVERSITY SCHOOL OF MEDICINE
依托单位国家：
美国
项目类别：
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/10206005
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 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 preventive intervention prospective risk prediction simulation social tool trauma exposure traumatic event traumatic stress traumatized children

项目摘要

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.

项目总结/文摘