An active learning framework for adaptive autism healthcare

项目摘要 神经精神障碍是非传染性疾病导致残疾的最大原因 全世界，占全球疾病负担的14%。目前的护理标准受到 主观性、交付不一致，以及随着等待名单的增加而访问受限。尤其是数据科学解决方案 人工智能(AI)可以移植到更普遍的移动工具上，并且不受临床使用的限制 在医疗环境中，医疗服务具有很大的潜力，可以补充甚至取代护理标准的某些方面。我们建议 开发一种新的数据科学解决方案，以应对最紧迫的心理健康负担之一--自闭症，这是一种 自1990年以来，患病率增加了200%以上，是当今增长最快的儿科问题之一，而且 有许多其他精神健康问题的代表。我们发明了一个原型移动系统，名为 你猜发生了什么(GW)，通过一个流动的社交媒体，非侵入性地将相机的焦点转向了孩子 以一种强化亲社会学习的方式与他/她的社会伴侣互动 测量儿童的发展性学习进度。在最简单的层面上，GW应用程序参与并 让孩子模仿手持智能手机屏幕上显示的以社交和情感为中心的提示 就在和孩子一起玩耍的人的眼睛上方。到目前为止的初步工作取得了积极的成果 用户反馈，父母和孩子高度参与度的证据，以及在 孩子的社会化。单个会话产生90秒丰富的社交视频和传感器数据， 为游戏提供了一个令人兴奋的机会，可以被动地生成标签训练库，从而实现 如果没有足够的领域，开发新的模型是极其困难的- 相关培训数据。我们的拨款计划将通过设计和优化游戏模式来探索这一机会， 为与领域相关的培训库的增长创建可重复使用的主动学习框架，并通过在 至少3个“自闭症特征感知”神经网络，可以检测儿童情绪、眼睛凝视和手势。我们的 该项目将表明，GW不仅可以将众包构建的新型AI模型自动 对儿童发展的重要特征进行分类-提供一种方法来应对人工智能在 今天的医学--但它也可以作为一种移动疗法，反复使用，以针对核心自闭症缺陷，同时 同时也在跟踪进度。

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.