Autism spectrum disorder is a developmental disorder characterized by significant social, communication, and behavioral challenges. Individuals diagnosed with autism, intellectual, and developmental disabilities (AUIDD) typically require long-term care and targeted treatment and teaching. Effective treatment of AUIDD relies on efficient and careful behavioral observations done by trained applied behavioral analysts (ABAs). However, this process overburdens ABAs by requiring the clinicians to collect and analyze data, identify the problem behaviors, conduct pattern analysis to categorize and predict categorical outcomes, hypothesize responsiveness to treatments, and detect the effects of treatment plans. Successful integration of digital technologies into clinical decision-making pipelines and the advancements in automated decision-making using Artificial Intelligence (AI) algorithms highlights the importance of augmenting teaching and treatments using novel algorithms and high-fidelity sensors. In this article, we present an AI-Augmented Learning and Applied Behavior Analytics (AI-ABA) platform to provide personalized treatment and learning plans to AUIDD individuals. By defining systematic experiments along with automated data collection and analysis, AI-ABA can promote self-regulative behavior using reinforcement-based augmented or virtual reality and other mobile platforms. Thus, AI-ABA could assist clinicians to focus on making precise data-driven decisions and increase the quality of individualized interventions for individuals with AUIDD.

中文翻译：

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发育障碍儿童的AI增强行为分析：建立精准治疗

自闭症谱系障碍是一种发展性障碍，其特征是严重的社交，沟通和行为挑战。被诊断患有自闭症，智力和发育障碍（AUIDD）的个体通常需要长期护理以及针对性的治疗和教学。对AUIDD的有效治疗依赖于训练有素的应用行为分析师（ABA）进行的有效而仔细的行为观察。但是，此过程要求临床医生收集和分析数据，识别问题行为，进行模式分析以分类和预测分类结果，假设对治疗的反应性以及检测治疗计划的效果，从而使ABA负担沉重。将数字技术成功集成到临床决策流程中，以及使用人工智能（AI）算法进行自动化决策的进步，凸显了使用新颖算法和高保真传感器增强教学和治疗的重要性。在本文中，我们介绍了一个AI增强的学习和应用行为分析（AI-ABA）平台，可为AUIDD个人提供个性化的治疗和学习计划。通过定义系统性实验以及自动数据收集和分析，AI-ABA可以使用基于增强的增强或虚拟现实以及其他移动平台来促进自我调节行为。因此，AI-ABA可以帮助临床医生专注于做出精确的数据驱动决策，并提高AUIDD患者个体化干预的质量。