A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism.

IF 7 Q1 MATHEMATICAL & COMPUTATIONAL BIOLOGY Annual Review of Biomedical Data Science Pub Date : 2023-08-10 Epub Date: 2023-05-03 DOI:10.1146/annurev-biodatasci-020722-125454
Peter Washington, Dennis P Wall
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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.

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自闭症神经精神表型的数据科学和机器学习综述和路线图。
自闭症谱系障碍是一种神经发育迟缓,至少每44名儿童中就有1名患有自闭症。与许多神经系统疾病表型一样,诊断特征是可观察的,可以随着时间的推移进行跟踪,并且可以通过适当的治疗和治疗来控制甚至消除。然而,自闭症和相关神经发育迟缓的诊断、治疗和纵向跟踪管道存在重大瓶颈,这为新的数据科学解决方案提供了机会,以增强和改造现有的工作流程,并为受影响的家庭提供更多的服务。许多研究实验室此前进行的几项努力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。我们回顾了有关自闭症行为量化的数字健康方法和使用数据科学的有益疗法的文献。我们描述了数字表型的病例对照研究和分类系统。然后,我们讨论了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,包括转化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正在面临的挑战和潜在的机遇。鉴于自闭症的异质性和相关行为的复杂性,这篇综述包含了与更广泛的神经行为分析和数字精神病学相关的见解。
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来源期刊
CiteScore
11.10
自引率
1.70%
发文量
0
期刊介绍: The Annual Review of Biomedical Data Science provides comprehensive expert reviews in biomedical data science, focusing on advanced methods to store, retrieve, analyze, and organize biomedical data and knowledge. The scope of the journal encompasses informatics, computational, artificial intelligence (AI), and statistical approaches to biomedical data, including the sub-fields of bioinformatics, computational biology, biomedical informatics, clinical and clinical research informatics, biostatistics, and imaging informatics. The mission of the journal is to identify both emerging and established areas of biomedical data science, and the leaders in these fields.
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