Autism spectrum disorder (ASD) research has yet to leverage Bbig data^on the same scale as other fields; however, advancements in easy, affordable data collection and analysis may soon make this a reality. Indeed, there has been a notable increase in research literature evaluating the effectiveness of machine learning for diagnosing ASD, exploring its genetic underpinnings, and designing effective interventions. This paper provides a comprehensive review of 45 papers utilizing supervised machine learning in ASD, including algorithms for classification and text analysis. The goal of the paper is to identify and describe supervised machine learning trends in ASD literature as well as inform and guide researchers interested in expanding the body of clinically, computationally, and statistically sound approaches for mining ASD data.

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Applied behavior analysis (ABA) is considered an effective treatment for individuals with autism spectrum disorder (ASD), and many researchers have further investigated factors associated with treatment outcomes. However, few studies have focused on whether treatment intensity and duration have differential influences on separate skills. The aim of the current study was to investigate how treatment intensity and duration impact learning across different treatment domains, including academic, adaptive, cognitive, executive function, language, motor, play, and social. Separate multiple linear regression analyses were used to evaluate these relationships. Participants included 1468 children with ASD, ages 18 months to 12 years old, M=7.57 years, s.d.=2.37, who were receiving individualized ABA services. The results indicated that treatment intensity and duration were both significant predictors of mastered learning objectives across all eight treatment domains. The academic and language domains showed the strongest response, with effect sizes of 1.68 and 1.85 for treatment intensity and 4.70 and 9.02 for treatment duration, respectively. These findings are consistent with previous research that total dosage of treatment positively influences outcomes. The current study also expands on extant literature by providing a better understanding of the differential impact that these treatment variables have across various treatment domains.

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Children with autism spectrum disorder (ASD) are at an increased risk of injury, making safety skills training essential. Whether such training is conducted in the natural environment or in contrived settings is an important consideration for generalization and safety purposes. Immersive virtual reality (VR) environments may offer the advantages of both contrived and natural environment training settings, providing structure to create repeated learning opportunities in a safe and realistic analogue of the natural environment. The current study evaluated the effectiveness of an immersive VR safety skills training environment in teaching 3 children with ASD to identify whether it is safe to cross the street. After modifications to the VR training environment, all 3 participants reached mastery criteria in both VR and natural environment settings. Findings suggest that immersive VR is a promising medium for the delivery of safety skills training to individuals with ASD.

The current study evaluated the effectiveness of a mobile application, Camp Discovery, designed to teach receptive language skills to children with autism spectrum disorder based on the principles of applied behavior analysis. Participants (N = 28) were randomly assigned to an immediate-treatment or a delayed-treatment control group. The treatment group made significant gains, p < .001, M = 58.1, SE = 7.54, following 4 weeks of interaction with the application as compared to the control group, M = 8.4, SE = 2.13. Secondary analyses revealed significant gains in the control group after using the application and maintenance of acquired skills in the treatment group after application usage was discontinued. Findings suggest that the application effectively teaches the targeted skills.Keywords Autism spectrum disorder . Applied behavior analysis . Computer-based intervention . Mobile application . Technology Advancements in technology continue to transform day-today living for individuals across populations, including individuals with autism spectrum disorder (ASD). The use of technology to assist individuals with ASD has been investigated in a variety of capacities, including augmented and alternative communication (Still, Rehfeldt, Whelan, May, & Dymond, 2014); prompting tools to assist with organization, self-management, time management, and task completion

The field of applied behavior analysis (ABA) has utilized telehealth for clinical supervision and caregiver guidance with research supporting the use of both modalities. Research demonstrating effectiveness is crucial, as behavior analysts must ensure the services they provide are effective in order to be ethical. With the increased need for patients to access more services via telehealth, due to the novel coronavirus (COVID-19) pandemic, the current study evaluated the efficacy of telehealth direct therapy to teach new skills to individuals with autism spectrum disorder (ASD). This study examined the utility of natural environment teaching and discrete trial training strategies provided over a videoconferencing platform to teach new skills directly to seven individuals with varying ASD severity levels. The targeted skills were taught solely through telehealth direct therapy with varying levels of caregiver support across participants and included skills in the language, adaptive, and social domains. In a multiple baseline design, all seven participants demonstrated mastery and maintenance for all targets; in addition, generalization to family members was assessed for some targets. The evidence suggests that telehealth is a modality that is effective and can be considered for all patients when assessing the appropriate location of treatment.Keywords telehealth direct therapy . discrete trial training . natural environment teaching . generalization

Ample research has shown the benefits of intensive applied behavior analysis (ABA) treatment for autism spectrum disorder (ASD); research that investigates the role of treatment supervision, however, is limited. The present study examined the relationship between mastery of learning objectives and supervision hours, supervisor credentials, years of experience, and caseload in a large sample of children with ASD ( = 638). These data were retrieved from a large archival database of children with ASD receiving community-based ABA services. When analyzed together via a multiple linear regression, supervision hours and treatment hours accounted for only slightly more of the observed variance ( = 0.34) than treatment hours alone ( = 0.32), indicating that increased supervision hours do not dramatically increase the number of mastered learning objectives. In additional regression analyses, supervisor credentials were found to have a significant impact on the number of mastered learning objectives, wherein those receiving supervision from a Board Certified Behavior Analyst (BCBA) mastered significantly more learning objectives. Likewise, the years of experience as a clinical supervisor showed a small but significant impact on the mastery of learning objectives. A supervisor's caseload, however, was not a significant predictor of the number of learning objectives mastered. These findings provide guidance for best practice recommendations.