  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.
