Deveau Nicholas, Washington Peter, Leblanc Emilie, Husic Arman, Dunlap Kaitlyn, Penev Yordan, Kline Aaron, Mutlu Onur Cezmi, Wall Dennis P
Biomedical Data Science, Stanford University, Stanford, 94305, California, United States.
Bioengineering, Stanford University, Stanford, 94305, California, United States.
Intell Based Med. 2022;6:100057. doi: 10.1016/j.ibmed.2022.100057. Epub 2022 Aug 24.
Digitally-delivered healthcare is well suited to address current inequities in the delivery of care due to barriers of access to healthcare facilities. As the COVID-19 pandemic phases out, we have a unique opportunity to capitalize on the current familiarity with telemedicine approaches and continue to advocate for mainstream adoption of remote care delivery. In this paper, we specifically focus on the ability of GuessWhat? a smartphone-based charades-style gamified therapeutic intervention for autism spectrum disorder (ASD) to generate a signal that distinguishes children with ASD from neurotypical (NT) children. We demonstrate the feasibility of using "in-the-wild", naturalistic gameplay data to distinguish between ASD and NT by children by training a random forest classifier to discern the two classes (AU-ROC = 0.745, recall = 0.769). This performance demonstrates the potential for GuessWhat? to facilitate screening for ASD in historically difficult-to-reach communities. To further examine this potential, future work should expand the size of the training sample and interrogate differences in predictive ability by demographic.
由于获得医疗保健设施存在障碍，数字交付的医疗保健非常适合解决当前医疗保健交付中的不公平问题。随着新冠疫情逐渐结束，我们有一个独特的机会利用当前对远程医疗方法的熟悉程度，并继续倡导远程护理交付的主流采用。在本文中，我们特别关注“猜猜是什么？”的能力，这是一种基于智能手机的猜谜式游戏化治疗干预方法，用于自闭症谱系障碍（ASD），以生成一个信号，将患有ASD的儿童与神经典型（NT）儿童区分开来。我们通过训练一个随机森林分类器来辨别这两个类别（AU-ROC = 0.745，召回率 = 0.769），证明了使用“自然状态下”的自然游戏玩法数据来区分患有ASD和NT的儿童的可行性。这一表现证明了“猜猜是什么？”在历史上难以触及的社区中促进ASD筛查的潜力。为了进一步研究这一潜力，未来的工作应该扩大训练样本的规模，并按人口统计学特征研究预测能力的差异。
