发育障碍儿童的AI增强行为分析：建立精确治疗
AI-Augmented Behavior Analysis for Children with Developmental Disabilities: Building Towards Precision Treatment
摘要：
自闭症谱系障碍是一种发育障碍，其特征是显著的社会，沟通和行为挑战。被诊断患有自闭症，智力和发育障碍（AUIDD）的人通常需要长期护理和有针对性的治疗和教学。AUIDD的有效治疗依赖于训练有素的应用行为分析师（ABA）进行的有效和仔细的行为观察。然而，这一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，并检测治疗计划的效果，从而使ABA负担过重。将数字技术成功整合到临床决策流程中，以及使用人工智能（AI）算法在自动决策方面的进步，凸显了使用新型算法和高保真传感器增强教学和治疗的重要性。在本文中，我们提出了一个AI增强学习和应用行为分析（AI-ABA）平台，为AUIDD个体提供个性化的治疗和学习计划。通过定义系统实验以及自动化数据汇集和分析，AI-ABA可以使用基于增强或虚拟现实和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患者个性化干预的质量。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
