Duke Autism Center of Excellence: A translational digital health and computational approach to early identification, outcome monitoring, and biomarker discovery in autism

杜克大学自闭症卓越中心：用于自闭症早期识别、结果监测和生物标志物发现的转化数字健康和计算方法

• 批准号: 10523403
• 负责人: Geraldine Dawson
• 金额: $ 241.5万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-09-07 至 2027-08-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10523403
• 关键词: 6 year old; Address; Age; Age-Months; Automated Clinical Decision Support; Base of the Brain; Behavior; Biological Markers; Birth; Blue Cross; Blue Shield; Brain; Caregivers; Caring; Child; Child Behavior; Clinic; Clinical; Clinical Trials; Code; Collaborations; Computer Vision Systems; Computing Methodologies; Data; Data Analyses; Data Science; Data Set; Development; Devices; Diagnosis; Discrimination; Early identification; Electronic Health Record; Elements; Engineering; Factor Analysis; Family; Future; Goals; Health Services Accessibility; Health system; Home; Infant; Intellectual functioning disability; Intervention; Language; Machine Learning; Measures; Medicaid; Medical; Methods; Monitor; Natural Language Processing; Nature; Neurosciences; North Carolina; Outcome; Outcome Measure; Parent-Child Relations; Participant; Pathway Analysis; Pattern; Pediatrics; Phase; Phenotype; Population; Population Heterogeneity; Predictive Value; Prevalence; Primary Health Care; Provider; Psychiatry; Psychology; Quality of life; Questionnaires; Research; Screening procedure; Stratification; Testing; Toddler; Universities; Vision; autism spectrum disorder; autistic; autistic children; base; behavioral outcome; biomarker discovery; care providers; clinical care; clinical decision support; computer science; data management; design; digital; digital health; gastrointestinal; health data; implementation science; improved; indexing; innovation; insight; literacy; machine learning method; member; multimodality; neglect; neural network; novel; novel strategies; outreach; prediction algorithm; predictive modeling; racial and ethnic; recruit; relating to nervous system; screening; sex; social attention; success; support tools; tool; usability

ABSTRACT – Overall The overall goal of the Duke Autism Center of Excellence is to use an innovative, translational digital health and computational approach to address the critical need for more effective autism screening tools, objective outcome measures, and brain-based biomarkers that can be used in clinical trials with young autistic children. An Administrative Core, Dissemination and Outreach Core, and Data Management and Analysis Core will support three Projects. Project 1 will recruit a large population of 16- to 30-month-old toddlers through primary care clinics to evaluate the accuracy of a remotely administered novel digital phenotyping application (app) for detecting early signs of autism. The app automatically quantifies observations of children’s behavior using computer vision analysis and is deployed on widely available devices. The usability of the app for longitudinal outcome monitoring will be assessed at 16-30, 36, and 48 months of age. The feasibility of using computer vision analysis to measure patterns of caregiver-child interactions from videos recorded at home will be explored. Project 2 will develop a complementary autism screening approach by using North Carolina Medicaid and Blue Cross Blue Shield claims data (N ~ 230,000, autism cases ~6,000) to create a generalizable autism prediction model based on routine health data collected from birth to 18 months. Then, using Duke University Health System electronic health records (EHR; N ~ 64,000, autism cases ~ 800), this Project will use natural language processing to assess the added predictive value of EHR elements not captured in claims data (e.g., clinician notes). Both data sets will be leveraged to gain insight into the nature and prevalence of medical conditions in infants and toddlers who are later diagnosed with autism. Projects 1 and 2 will collaboratively engage primary care providers and other stakeholders to design an automated clinical decision support tool for autism screening that, in the future, could be integrated into the primary care provider’s clinical workflow. Project 3 will use an innovative machine learning computational method to develop a multimodal biomarker that combines features of electroencephalographic (EEG) activity and synchronized measures of children’s behavior (e.g., social attention) automatically coded via computer vision analysis, with a focus on neural connectivity measured via traditional methods (coherence, phase-lag index) and novel neural network analysis methods (discriminative cross-spectral factor analysis) developed by our team. This multimodal approach will be evaluated in 3–6-year-old autistic children without intellectual disability (ID), age- and sex-matched neurotypical children, and autistic children with ID (IQ <= 70). Across Projects, our Center’s team will share cutting-edge computational methods to develop new tools that can address long-standing barriers to optimal care and enhanced quality of life for autistic children and their families.

摘要 - 总体 杜克自闭症卓越中心的总体目标是使用创新的，翻译的数字健康和 计算方法以满足对更有效自闭症筛查工具的关键需求，客观结果 措施和基于大脑的生物标志物，可用于与年轻攻击性儿童的临床试验中。一个 行政核心，传播和推广核心以及数据管理和分析核心将支持 三个项目。项目1将通过初级保健诊所招募大量16至30个月大的幼儿 评估用于检测的新型数字表型应用程序（APP）的准确性 自闭症的早期迹象。该应用程序会使用计算机视觉自动量化对儿童行为的观察 分析并部署在广泛可用的设备上。该应用程序用于纵向结果监视的可用性 将在16-30、36和48个月中评估。使用计算机视觉分析测量的可行性 将探讨来自家里录制的视频的护理人孩子互动的模式。项目2将开发 通过使用北卡罗来纳州医疗补助和蓝十字蓝盾索赔，完全自闭症筛查方法 数据（n〜230,000，自闭症案例〜6,000）以基于例行程序创建可推广的自闭症预测模型 从出生到18个月收集的健康数据。然后，使用杜克大学卫生系统电子健康 记录（EHR; N〜64,000，自闭症案例〜800），该项目将使用自然语言处理来评估 索赔数据中未捕获的EHR元素的预测价值（例如临床注释）。两个数据集将是 利用以深入了解婴儿和幼儿的医疗状况的性质和患病率 后来被诊断出患有自闭症。项目1和2将协作与初级保健提供者和其他 利益相关者为自闭症筛查设计自动临床决策支持工具，将来可以 被整合到初级保健提供者的临床工作流程中。项目3将使用创新的机器学习 开发一种结合脑电图特征的多模式生物标志物的计算方法 （EEG）活动和同步儿童行为的度量（例如社会关注）自动编码 计算机视觉分析，重点是通过传统方法测得的神经连通性（相干， 相位滞后指数）和新型神经网络分析方法（判别性跨光谱因子分析） 由我们的团队开发。这种多模式方法将在没有3-6岁的加速儿童中评估 智力残疾（ID），年龄和性别匹配的神经型儿童以及患有ID的儿童（IQ <= 70）。 在整个项目中，我们中心的团队将共享尖端的计算方法，以开发可以 应对加速儿童及其家人的最佳护理和增强生活质量的长期障碍。