An active learning framework for adaptive autism healthcare

适应性自闭症医疗保健的主动学习框架

• 批准号: 10716509
• 负责人: Dennis Paul Wall
• 金额: $ 46.32万
• 依托单位: STANFORD UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-08 至 2027-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10716509
• 关键词: 8 year old; Accounting; Active Learning; Adaptive Behaviors; Address; Age; Artificial Intelligence; Awareness; Behavior; Behavior Therapy; Caring; Cellular Phone; Child; Child Development; Childhood; Classification; Clinical; Clip; Complement; Complex; Computer Vision Systems; Crowding; Data; Data Collection; Data Science; Data Set; Databases; Development; Disease; Emotional; Emotions; Ensure; Entropy; Eye; Family; Feedback; Foundations; Frequencies; Gestures; Grant; Growth; Hand; Health; Healthcare; Human; Image; Individual; Intervention; Label; Language; Learning; Libraries; Liquid substance; Manuals; Measures; Medicine; Mental Health; Metadata; Modeling; Names; Neural Network Simulation; Parents; Pattern; Performance; Phenotype; Play; Prevalence; Probability; Process; Risk; Running; Secure; Social Behavior; Socialization; Symptoms; System; Systems Development; Techniques; Testing; Time; Training; Waiting Lists; Work; annotation system; autism spectrum disorder; autistic children; behavior prediction; burden of illness; catalyst; crowdsourcing; data library; data sharing; deep learning; deep learning model; deep neural network; design; digital health; digital treatment; disability; feasibility testing; feature detection; feature extraction; feature selection; gaze; global health; insight; interest; invention; iterative design; learning progression; learning strategy; mobile application; model design; model development; neural network; neuropsychiatric disorder; novel; patient engagement; personalized intervention; personalized predictions; privacy preservation; prototype; sensor; showing emotion; simulation; social; social communication; social engagement; success; targeted treatment; therapy design; tool; treatment effect; trustworthiness; uptake; user-friendly

Project Summary Neuropsychiatric disorders are the single greatest cause of disability due to non-communicable disease worldwide, accounting for 14% of the global burden of disease. The current standards of care suffer from subjectivity, inconsistent delivery, and limited access with growing waitlists. Data science solutions, in particular artificial intelligence (AI) that can port to more ubiquitous mobile tools and that are not restricted for use in clinical settings, have great potential to complement or even replace aspects of the standards of care. We propose to develop a novel data science solution for one of the most pressing mental health burdens, autism, which is up in prevalence by more than 200% since 1990, among the fastest growing pediatric concerns today, and highly representative of many other mental health conditions. We have invented a prototype mobile system called Guess What (GW) that noninvasively turns the focus of the camera on the child through a fluid social engagement with his/her social partner in a way that reinforces prosocial learning while simultaneously measuring the child’s developmental learning progress. At its simplest level, the GW app engages and challenges the child to imitate social and emotion-centric prompts shown on the screen of a smartphone held just above the eyes of the individual with whom the child is playing. Preliminary work to-date resulted in positive user feedback, evidence of high engagement for both the parents and children, and meaningful gains in socialization in the child. A single session produces 90 seconds of enriched social video and sensor data, opening up an exciting opportunity for the game play to passively generate labeled training libraries that enable the development of novel models that are extremely difficult to build without sufficient amounts of domain- relevant training data. Our grant plan will explore this opportunity by designing and optimizing game modes, creating a reusable active learning framework for growth of domain-relevant training libraries, and by creating at least 3 “autism-feature-aware” neural networks that detect child emotion, eye gaze, and hand gestures. Our project will show that GW can not only gamify crowdsourced construction of novel AI models that automatically classify important features of child development – providing a way to address many challenges with AI in medicine today -- but that it can also serve as a mobile therapy for repeat use to target core autism deficits while also tracking progress at the same time.

项目摘要 神经精神疾病是由于非通讯疾病而导致的疾病的最大原因 全球，占全球疾病烧伤的14％。目前的护理标准遭受 主观性，交付不一致以及越来越多的候补名单的访问权限。数据科学解决方案，特别是 人工智能（AI）可以移植到更加普遍的移动工具，并且不受限制用于临床 设置，有很大的潜力补充甚至取代护理标准的各个方面。我们建议 为最紧迫的心理健康伯恩斯（Autism）开发一种新颖的数据科学解决方案，这是 自1990年以来的患病率超过200％，这是当今增长最快的小儿问题之一，高度高度 代表许多其他心理健康状况。我们发明了一个原型移动系统，称为 猜猜什么（GW）是什么（无创的）将相机的焦点转移到孩子身上的液体社交 与他/她的社会伴侣互动，以加强亲社会学习的方式 衡量孩子的发展学习进步。在最简单的层面上，Golden Wed App参与了 挑战孩子模仿智能手机屏幕上显示的社交和以情感为中心的提示 在孩子们玩耍的人的眼睛上方。迄今为止的初步工作导致积极 用户反馈，父母和孩子的高参与证据以及有意义的收益 孩子的社会化。单个会话产生90秒的丰富社交视频和传感器数据， 为游戏打开一个激动人心的机会，以被动地产生标签的培训库，以启用 没有足够数量的领域 - 相关培训数据。我们的赠款计划将通过设计和优化游戏模式来探索这个机会， 创建可重复使用的主动学习框架，以增长与领域相关的培训库，并通过创建 至少有3个“自闭症感知”神经网络，可检测儿童情绪，眼睛注视和手势。我们的 项目将表明，GW不仅可以自动构造新型AI模型 对儿童发展的重要特征进行分类 - 提供一种解决AI的许多挑战的方法 今天的医学 - 但它也可以作为一种移动疗法，以重复用于靶向核心自闭症定义 同时跟踪进度。