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：自闭症谱系障碍（自闭症）是一种神经发育迟缓疾病，至少影响每44名儿童中的1人。与许多神经系统疾病表型相似，其诊断特征具有可观察性、可随时间追踪，并通过适当的治疗和干预得到管理甚至消除。然而，在自闭症及相关发育迟缓的诊断、治疗和纵向追踪流程中存在重大瓶颈，这为新型数据科学解决方案创造了机遇，以增强和改造现有工作流程，并为更多受影响的家庭提供获取服务的途径。此前多个研究团队的努力已推动针对自闭症儿童的数字化诊断和数字化疗法取得显著进展。我们综述了利用数据科学进行自闭症行为量化分析的数字健康方法文献，介绍了病例对照研究及数字表型的分类系统。进而讨论了整合自闭症相关行为机器学习模型的数字化诊断与治疗手段，包括其实验转化应用中必须解决的关键因素。最后，我们阐述了自闭症数据科学领域当前面临的挑战与潜在机遇。鉴于自闭症的异质性特征及相关行为的复杂性，本综述包含的见解对更广泛的神经行为分析与数字精神病学具有参考价值。