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.
翻译:自闭症谱系障碍是一种发育障碍,其特点是严重的社会、通信和行为挑战。被诊断患有自闭症、智力和发育残疾(AUIDD)的个人通常需要长期护理和有针对性的治疗和教学。对AUIDD的有效治疗依赖经过培训的应用行为分析师(ABAs)进行的高效和谨慎的行为观察。然而,这一过程使ABA负担过重,要求临床医生收集和分析数据,查明问题行为,进行模式分析,对治疗结果进行分类和预测,虚度反应,并发现治疗计划的效果。将数字技术成功地纳入临床决策管道,以及利用人工智能(AI)算法在自动化决策方面取得进展。 人工智能(AI-ABA)算法强调利用新型算法和高偏执感传感器加强教学和治疗的重要性。在本篇文章中,我们提出了一个AI-AU学习和应用行为分析(AI-ABA)平台,为AUIDD个人提供个性化治疗和学习计划。通过对自动化数据收集和分析确定系统实验,AI-ABA系统化技术在自动化数据收集和分析方面进行自动分析,使AI-ABA增强个人的自我驱动性平台,可以促进个人的自我驱动性分析。