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

arXiv: https://arxiv.org/abs/2303.03577 · HTML: https://mlsanity.com/paper/2303.03577/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 133 references that are in the map.
- First posted on arXiv 7 Mar 2023.

## Builds on (most cited first, 9 of 9)

- [A Survey on Bias and Fairness in Machine Learning](https://mlsanity.com/paper/1908.09635/) (2019)
- [Machine Learning with a Reject Option: A survey](https://mlsanity.com/paper/2107.11277/) (2021)
- [Personalized Machine Learning for Robot Perception of Affect and Engagement in Autism Therapy](https://mlsanity.com/paper/1802.01186/) (2018)
- [Deep Learning for Neuroimaging-based Diagnosis and Rehabilitation of Autism Spectrum Disorder: A Review](https://mlsanity.com/paper/2007.01285/) (2020)
- [Superpower Glass: Delivering Unobtrusive Real-time Social Cues in Wearable Systems](https://mlsanity.com/paper/2002.06581/) (2020)
- [Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study](https://mlsanity.com/paper/2012.08678/) (2020)
- [Semi-Supervised Learning and Data Augmentation in Wearable-based Momentary Stress Detection in the Wild](https://mlsanity.com/paper/2202.12935/) (2022)
- [Machine learning approach for early detection of autism by combining questionnaire and home video screening](https://mlsanity.com/paper/1703.06076/) (2017)
- [Towards Automatic Screening of Typical and Atypical Behaviors in Children With Autism](https://mlsanity.com/paper/1907.12537/) (2019)

## Built on by (most cited first, 11 of 11)

- [A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study](https://mlsanity.com/paper/2308.14245/) (2023)
- [Computer Vision Estimation of Emotion Reaction Intensity in the Wild](https://mlsanity.com/paper/2303.10741/) (2023)
- [Personalized Prediction of Recurrent Stress Events Using Self-Supervised Learning on Multimodal Time-Series Data](https://mlsanity.com/paper/2307.03337/) (2023)
- [Ensemble Modeling of Multiple Physical Indicators to Dynamically Phenotype Autism Spectrum Disorder](https://mlsanity.com/paper/2408.13255/) (2024)
- [Self-Supervised Learning for Audio-Based Emotion Recognition](https://mlsanity.com/paper/2307.12343/) (2023)
- [Fine-tuning a multimodal large language model for clinician-grade autism behavioral scoring from short home videos](https://mlsanity.com/paper/2606.27484/) (2026)
- [Personalization of Stress Mobile Sensing using Self-Supervised Learning](https://mlsanity.com/paper/2308.02731/) (2023)
- [Personalization of Affective Models to Enable Neuropsychiatric Digital Precision Health Interventions: A Feasibility Study](https://mlsanity.com/paper/2311.12812/) (2023)
- [Evaluating Large Language Models for Anxiety and Depression Classification using Counseling and Psychotherapy Transcripts](https://mlsanity.com/paper/2407.13228/) (2024)
- [Towards Equitable ASD Diagnostics: A Comparative Study of Machine and Deep Learning Models Using Behavioral and Facial Data](https://mlsanity.com/paper/2411.05880/) (2024)
- [A multimodal large language model for evidence-based autism spectrum disorder screening](https://mlsanity.com/paper/2609.16464/) (2026)

## Abstract (arXiv)

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.