Research Project 1

研究项目1

• 批准号: 10672632
• 负责人: Sisi Ma
• 金额: $ 65.16万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-05 至 2028-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10672632
• 关键词: Address; Administrator; Adolescent; Aggressive behavior; Categories; Characteristics; Child; Child Abuse and Neglect; Child Health; Child Welfare; Classification; Clinical; Clinical Decision Support Systems; Collaborations; Collection; Complex; Data Science; Data Set; Dedications; Disease; Doctor of Philosophy; Economics; Education; Ensure; Etiology; Family; Family member; Genetic; Goals; Health; Impairment; Individual; Infrastructure; Intervention; Knowledge; Mental Depression; Mental Health; Methods; Minnesota; Modeling; Obesity; Outcome; Performance; Population; Positioning Attribute; Post-Traumatic Stress Disorders; Prevention; Primary Care; Public Health Informatics; Recommendation; Recording of previous events; Research; Research Project Grants; Resources; Risk; Risk Factors; Risk Management; Risk Reduction; Scientist; Self-Injurious Behavior; Severities; Sexual abuse; Signal Transduction; Substance abuse problem; System; Testing; Time; Translating; Translations; Universities; Work; behavioral health; causal model; clinical translation; demographics; design; functional disability; improved; individualized prevention; insight; machine learning pipeline; machine learning prediction; maltreatment; medical schools; member; model building; multidisciplinary; neglect; outreach; pediatrician; personalized medicine; personalized risk prediction; physical abuse; physical conditioning; predictive modeling; prevent; psychobiologic; risk prediction; social; success; support tools; tool; translational model; traumatic stress; trustworthiness

SUMMARY/ABSTRACT – PROJECT 1 Project 1 of the Center on Causal Data Science for Child and Adolescent Maltreatment Prevention (CHAMP Center) will involve determining predictive and causal models for many categories of maltreatment exposures and their consequential outcomes, and developing a scalable set of decision support tools (DSTs) from these models. These DSTs will be designed to guide decisions by practitioners in a position to prevent maltreatment and mitigate the severity of maltreatment-related outcomes. Given that intervention on targets that are non-causal/non-etiological factors cannot improve health or adaptive functioning, such decisions require that that the practitioner can confidently: 1) identify etiological factors that cause health or functional impairments, and 2) target the external or internal etiological factors relevant for these impairments for a particular child and family. This becomes especially challenging when etiologies are complex and interventions must be customized to precisely target diverse sets of causes, as is often true for children and families who meet criteria for maltreatment-related interventions. The goal of Project 1 is to determine models and to develop a set of DSTs to guide such decisions. We will do this by applying advanced causal data science methods to several large, relevant data sets to derive valid causal models for several categories of maltreatment exposure (sexual abuse, physical abuse, neglect) and maltreatment- related outcomes (post-traumatic stress disorder, depression, self-harm, aggression, substance abuse, obesity, functional impairment). We will integrate these analyses with machine learning predictive classification to identify which predictive models can accurately classify children into risk categories. We will work closely with members of the Resource Core to develop the models and of the Dissemination & Outreach Core to engage the full spectrum of relevant stakeholders (domain experts, primary care pediatricians, child welfare workers, behavioral health clinicians, agency administrators, children, and family members) to ensure that the tools Project 1 develops are effective, practical, trustworthy, and safe. One of the DSTs that Project 1 develops will form the basis for Project 2, which will test this tool in a field trial. Insights from Project 2 will be essential for Project 1 to iteratively improve on the DSTs it develops.

摘要/摘要 - 项目1 儿童和青少年虐待预防因果数据科学中心的项目1 （冠军中心）将涉及确定许多类别的预测性和因果模型 虐待暴露及其结果结果，并制定一套可扩展的决定 这些模型的支持工具（DST）。这些DST将旨在指导决策 从业者可以防止虐待并减轻与虐待有关的严重性 结果。鉴于对非因果/非生物因素的目标的干预不能 改善健康或适应性功能，此类决策要求从业者可以 自信：1）确定导致健康或功能障碍的病因，2）针对 与特定儿童和家庭有关的这些障碍有关的外部或内部病因因素。 当病因复杂时，这变得尤其具有挑战性，干预必须是 对精确针对各种原因集的定制，对于儿童和家庭通常是正确的 符合与虐待有关的干预措施的标准。项目1的目标是确定模型和 开发一组DST来指导此类决策。我们将通过应用高级因果数据来做到这一点 科学方法用于几个大型相关数据集，以得出几种有效的因果模型 虐待类别（性虐待，身体虐待，忽视）和虐待类别 相关结果（创伤后应激障碍，抑郁症，自我伤害，侵略性，底物 滥用，肥胖，功能障碍）。我们将将这些分析与机器学习相结合 预测性分类以确定哪些预测模型可以准确地将儿童分类为风险 类别。我们将与资源核心成员紧密合作，以开发模型和 传播和外展核心参与相关利益相关者（领域） 专家，初级保健儿科医生，儿童福利工人，行为健康临床医生，代理商 管理员，儿童和家庭成员），以确保工具项目1的开发是 有效，实用，值得信赖和安全。项目1开发的DST之一将构成基础 对于项目2，将在现场试验中测试此工具。项目2的见解对于项目至关重要 1迭代地改进其发展的DST。