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Abstract：
Autism spectrum disorder (autism) is a neurodevelopmental delay that 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. However, there are major bottlenecks in the diagnostic, therapeutic, and longitudinal tracking pipelines for autism and related neurodevelopmental delays, creating an opportunity for novel data science solutions to augment and transform existing workflows and provide increased access to services for affected families. Several efforts previously conducted by a multitude of research labs have spawned great progress toward improved digital diagnostics and digital therapies for children with autism. We review the literature on digital health methods for autism behavior quantification and beneficial therapies using data science. We describe both case-control studies and classification systems for digital phenotyping. We then discuss digital diagnostics and therapeutics that integrate machine learning models of autism-related behaviors, including the factors that must be addressed for translational use. Finally, we describe ongoing challenges and potential 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 that are relevant to neurological behavior analysis and digital psychiatry more broadly.
摘要：
自闭症谱系障碍（自闭症）是一种神经发育迟缓，至少影响44名儿童中的1名。像许多神经系统疾病表型一样，诊断特征是可观察的，可以随着时间的推移跟踪，并且可以通过适当的治疗和治疗来管理甚至消除。然而,诊断存在主要瓶颈，治疗性的,自闭症和相关神经发育迟缓的纵向跟踪管道，为新的数据科学解决方案创造机会，以增强和改变现有的工作流程，并为受影响的家庭提供更多的服务。先前由众多研究实验室进行的几项努力已经在改善自闭症儿童的数字诊断和数字治疗方面取得了重大进展。我们回顾了使用数据科学进行自闭症行为量化和有益治疗的数字健康方法的文献。我们描述了病例对照研究和数字表型分类系统。然后，我们讨论整合自闭症相关行为的机器学习模型的数字诊断和治疗方法，包括翻译使用必须解决的因素。最后,我们描述了自闭症数据科学领域的持续挑战和潜在机遇.鉴于自闭症的异质性和相关行为的复杂性，这篇综述包含了更广泛的与神经行为分析和数字精神病学相关的见解。生物医学数据科学年度评论的预期最终在线出版日期，第六卷是2023年8月。请参阅http://www。annualreviews.org/page/journal/pubdates的订正估计数。
