Accepted for/Published in: JMIR Medical Informatics
Date Submitted: Feb 6, 2021
Date Accepted: Apr 29, 2021
Warning: This is an author submission that is not peer-reviewed or edited. Preprints - unless they show as "accepted" - should not be relied on to guide clinical practice or health-related behavior and should not be reported in news media as established information.
Identifying Challenging Behavior Profiles and Exploring their Impact on Treatment Efficacy in Autism Spectrum Disorder using Unsupervised Machine Learning
ABSTRACT
Background:
Challenging behaviors are prevalent among individuals with autism spectrum disorder (ASD); however, research exploring the impact of challenging behaviors on treatment response is lacking.
Objective:
The purpose of the current study was to identify subtypes of ASD based on engagement in different challenging behaviors and evaluate differences in treatment response between subgroups.
Methods:
Retrospective data on challenging behaviors and treatment progress for 854 children with ASD were analyzed. First, participants were clustered based on eight observed challenging behaviors using k-means. Next, a multiple linear regression analysis was performed to find significant interactions between skill mastery and treatment hours, cluster assignment, and gender.
Results:
Seven diverse clusters were identified, which demonstrated a single dominant challenging behavior. For some clusters, significant differences in treatment response were found. Specifically, a cluster characterized by stereotypy was found to have significantly higher levels of skill mastery than clusters characterized by self-injurious behavior and aggression.
Conclusions:
These findings have implications on the treatment of individuals with ASD. First, self-injurious behavior and aggression were prevalent among participants with the poorest treatment response, thus interventions targeting these challenging behaviors may be worth prioritizing. Furthermore, the use of unsupervised machine learning models to identify subtypes of ASD shows promise.
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Copyright
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