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 杜克大学自闭症卓越中心的总体目标是利用创新的、转化性的数字健康和 计算方法可满足对更有效的自闭症筛查工具、客观结果的迫切需求 措施以及可用于自闭症儿童临床试验的基于大脑的生物标志物。 行政核心、传播和外展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。 评估远程管理的新型数字表型分析应用程序 (app) 的准确性，以进行检测 该应用程序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控的可用性。 将在16-30、36和48个月龄时评估使用计算机视觉分析来测量的可行性。 项目 2 将探索在家中录制的视频中的看护者与儿童互动的模式。 使用北卡罗来纳州医疗补助和蓝十字蓝盾索赔的补充自闭症筛查方法 数据（N ~ 230,000，自闭症病例 ~6,000），以创建基于常规的通用自闭症预测模型 然后，使用杜克大学健康系统电子健康收集从出生到 18 个月的健康数据。 记录（EHR；N ~ 64,000，自闭症病例 ~ 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 要素的附加预测价值（例如，临床医生注释）将被纳入。 用于深入了解婴幼儿健康状况的性质和患病率 后来被诊断患有自闭症的人，项目 1 和 2 将与初级保健提供者和其他人员合作。 利益相关者设计一个用于自闭症筛查的自动化临床决策支持工具，该工具在未来可以 项目 3 将使用创新的机器学习技术，将其集成到初级保健提供者的临床工作流程中。 开发结合脑电图特征的多模态生物标志物的计算方法 （脑电图）活动和儿童行为的同步测量（例如，社会注意力）通过自动编码 计算机视觉分析，重点关注通过传统方法（一致性、 相位滞后指数）和新颖的神经网络分析方法（判别性跨谱因子分析） 我们的团队开发了这种多模式方法，将在 3-6 岁的自闭症儿童中进行评估。 智力障碍 (ID)、年龄和性别匹配的神经正常儿童以及患有 ID 的自闭症儿童 (IQ <= 70)。 在各个项目中，我们中心的团队将分享尖端的计算方法来开发新工具，这些工具可以 解决自闭症儿童及其家庭获得最佳护理和提高生活质量的长期障碍。