Leveraging routinely collected health data to improve understanding of language development in children identified as late talkers

摘要--总体 杜克大学自闭症卓越中心的总体目标是使用创新的、可转换的数字健康和 解决对更有效的自闭症筛查工具的迫切需求的计算方法，客观结果 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记物。一个 行政核心、传播和外联核心以及数据管理和分析核心将提供支助 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应用程序(APP)用于检测的准确性 自闭症的早期症状。这款应用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局监测的可用性 将在16-30、36和48个月龄时进行评估。利用计算机视觉分析进行测量的可行性 将探索在家中录制的视频中的照顾者与儿童互动的模式。项目2将开发一个 使用北卡罗来纳州医疗补助和蓝十字蓝盾声称的互补自闭症筛查方法 数据(N~230,000例自闭症病例~6,000例)，建立基于常规的可推广的自闭症预测模型 收集从出生到18个月的健康数据。然后，使用杜克大学健康系统电子健康 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 增加了索赔数据(例如，临床医生记录)中未记录的EHR元素的预测价值。这两个数据集都将 了解婴儿和幼儿的健康状况的性质和流行率 后来被诊断出患有自闭症。项目1和2将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化临床决策支持工具，未来可以 整合到初级保健提供者的临床工作流程中。项目3将使用一种创新的机器学习 开发结合脑电特征的多模式生物标志物的计算方法 (EEG)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，重点是通过传统方法测量的神经连通性(一致性， 相位滞后指数)和新的神经网络分析方法(鉴别互谱因子分析) 由我们的团队开发。这种多模式方法将在3-6岁的自闭症儿童中进行评估， 智障(ID)，年龄和性别匹配的神经典型儿童，以及智商=70的自闭症儿童。 在各个项目中，我们中心的团队将分享尖端计算方法，以开发能够 解决长期存在的阻碍自闭症儿童及其家庭获得最佳护理和提高生活质量的问题。

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.