Autism Spectrum Disorder (autism) is a neurodevelopmental delay which affects at least 1 in 44 children. Like many neurological disorder phenotypes, the diagnostic features are observable, can be tracked over time, and can be managed or even eliminated through proper therapy and treatments. Yet, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide access to services for more affected families. Several prior efforts conducted by a multitude of research labs have spawned great progress towards improved digital diagnostics and digital therapies for children with autism. We review the literature of digital health methods for autism behavior quantification using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics which integrate machine learning models of autism-related behaviors, including the factors which must be addressed for translational use. Finally, we describe ongoing challenges and potent opportunities for the field of autism data science. Given the heterogeneous nature of autism and the complexities of the relevant behaviors, this review contains insights which are relevant to neurological behavior analysis and digital psychiatry more broadly.
翻译:自闭症光谱障碍(Autism Spectrum Astics)是一个神经发育迟缓的神经发育迟缓,至少影响到44名儿童中的1人。像许多神经系统紊乱苯型一样,诊断特征是可见的,可以随时间跟踪,可以通过适当的治疗和治疗加以管理甚至消除。然而,自闭症和相关延误的诊断、治疗和纵向跟踪管道存在重大瓶颈,为新颖的数据科学解决方案创造了机会,以扩大和改造现有工作流程,并为受影响家庭提供服务。许多研究实验室先前开展的若干努力在改进自闭症儿童的数字诊断和数字治疗方面取得了巨大进展。我们审查了用于自闭症行为量化的数字保健方法文献,我们用数据科学对案例控制研究和数字口腔分类系统进行了描述。我们随后讨论了将自闭症相关行为机学模型整合在一起的数码诊断和治疗方法,包括必须用于翻译使用的各种因素。最后,我们描述了自闭症数据科学领域目前存在的挑战和强大的机遇。我们审查了自闭症的可变性性质和较复杂的神经性分析。本次审查含有与神经诊断的复杂性分析。</s>