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

利用定期收集的健康数据来提高对说话晚的儿童的语言发展的理解

• 批准号: 10838732
• 负责人: Geraldine Dawson
• 金额: $ 26.1万
• 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/10838732
• 关键词: 6 year old; Address; Age; Age Months; Automated Clinical Decision Support; Behavior; Biological Markers; Birth; Blue Cross; Blue Shield; Brain; Caregivers; Caring; Child; Child Behavior; Clinical; Clinical Trials; Code; Collaborations; Computer Vision Systems; Computing Methodologies; Data; Data Analyses; Data Science; Data Set; Development; Devices; Diagnosis; Discrimination; Early Diagnosis; Electroencephalography; Electronic Health Record; Elements; Engineering; Equity; Ethnic Origin; Factor Analysis; Family; Future; Goals; Health Services Accessibility; Health system; Home; Infant; Intellectual functioning disability; Intervention; Language; Language Development; Machine Learning; Measures; Medicaid; Medical; Methods; Monitor; Natural Language Processing; Nature; Neurosciences; North Carolina; Outcome; Outcome Measure; Parent-Child Relations; Participant; Pathway Analysis; Pattern; Patterns of Care; Pediatrics; Phase; Phenotype; Population; Population Heterogeneity; Predictive Value; Prevalence; Provider; Psychiatry; Psychology; Qualifying; Quality of life; Questionnaires; Race; Research; Screening procedure; Stratification; Testing; Toddler; Universities; Video Recording; autism spectrum disorder; autistic; autistic children; behavioral outcome; brain based; clinical care; clinical decision support; computer science; data management; design; digital; digital health; electronic health data; gastrointestinal; health data; implementation science; improved; indexing; innovation; insight; literacy; machine learning method; member; multimodality; neglect; neural; neural network; novel; novel strategies; outreach; prediction algorithm; predictive modeling; primary care clinic; primary care provider; recruit; remote administration; 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，转化的数字健康和 计算方法，以解决更有效的自闭症筛查工具，客观结果的关键需求 措施，以及可用于自闭症儿童临床试验的脑生物标志物。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应用程序（app）检测的准确性 自闭症的早期症状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。应用程序用于纵向结局监测的可用性 将在16-30、36和48个月大时进行评估。利用计算机视觉分析进行测量的可行性 将探讨在家中录制的视频中的儿童与儿童互动模式。项目2将开发一个 通过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），以创建基于常规的可推广自闭症预测模型 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 增加了索赔数据中未捕获的EHR元素的预测值（例如，临床医师记录）。这两个数据集将 用于深入了解婴儿和幼儿的医疗状况的性质和患病率， 后来被诊断为自闭症项目1和2将与初级保健提供者和其他 利益相关者设计一个自闭症筛查的自动化临床决策支持工具，在未来， 整合到初级保健提供者的临床工作流程中。Project 3将使用创新的机器学习 开发结合脑电图特征的多模态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，社会关注）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接性（相干性， 相位滞后指数）和新的神经网络分析方法（判别交叉谱因子分析） 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， 智力残疾（ID）、年龄和性别匹配的神经正常儿童和患有ID（IQ <= 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的计算方法，以开发新的工具， 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。

项目成果

Adaptive Behavior in Young Autistic Children: Associations with Irritability and ADHD Symptoms.

年轻自闭症儿童的适应性行为：与烦躁和多动症症状的关联。

• DOI: 10.1007/s10803-022-05753-2
• 发表时间: 2022
• 期刊: Journal of autism and developmental disorders
• 影响因子: 3.9
• 作者: Carpenter,KimberlyLH;Davis,NaomiO;Spanos,Marina;Sabatos-DeVito,Maura;Aiello,Rachel;Baranek,GraceT;Compton,ScottN;Egger,HelenL;Franz,Lauren;Kim,Soo-Jeong;King,BryanH;Kolevzon,Alexander;McDougle,ChristopherJ;Sanders,Kevin

A tablet-based game for the assessment of visual motor skills in autistic children.

• DOI: 10.1038/s41746-023-00762-6
• 发表时间: 2023-02-03
• 期刊: NPJ digital medicine
• 影响因子: 15.2

A Six-Minute Measure of Vocalizations in Toddlers with Autism Spectrum Disorder.

• DOI: 10.1002/aur.2293
• 发表时间: 2020-08
• 期刊: Autism research : official journal of the International Society for Autism Research
• 作者: Tenenbaum EJ;Carpenter KLH;Sabatos-DeVito M;Hashemi J;Vermeer S;Sapiro G;Dawson G
• 通讯作者: Dawson G

Estimating Uncertainty Intervals from Collaborating Networks.

• DOI: pii: 257
• 发表时间: 2021-01
• 期刊: Journal of machine learning research : JMLR

Computer Vision Analysis for Quantification of Autism Risk Behaviors.

• DOI: 10.1109/taffc.2018.2868196
• 发表时间: 2021-01
• 期刊: IEEE transactions on affective computing
• 影响因子: 11.2
• 作者: Hashemi J;Dawson G;Carpenter KLH;Campbell K;Qiu Q;Espinosa S;Marsan S;Baker JP;Egger HL;Sapiro G
• 通讯作者: Sapiro G