自闭症神经精神表型的数据科学和机器学习的回顾和路线图
A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism
摘要：
自闭症谱系障碍（Autism Spectrum Disorder）是一种神经发育迟缓，每44名儿童中至少有1名受到影响。与许多神经系统疾病表型一样，诊断特征是可观察的，可以随时间推移进行跟踪，并且可以通过适当的治疗和治疗进行管理甚至消除。然而，在自闭症的诊断、治疗和纵向跟踪管道以及相关延迟方面存在重大瓶颈，这为新型数据科学解决方案创造了机会，以增强和改变现有的工作流程，并为更多受影响的家庭提供服务。许多研究实验室先前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巨大进展。我们回顾了使用数据科学进行自闭症行为量化的数字健康方法的文献。我们描述了病例对照研究和分类系统的数字表型。然后，我们讨论数字诊断和治疗，其中集成了自闭症相关行为的机器学习模型，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，这篇综述包含了与神经行为分析和数字精神病学更广泛相关的见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
