A Predictive Multimodal Framework to Alert Caregivers of Problem Behaviors for Children with ASD (PreMAC)
Zhaobo K Zheng1, John E Staubitz2, Amy S Weitlauf2,3
1Department of Mechanical Engineering, Vanderbilt University, Nashville, TN 37240, USA.
Insights
Caregivers can now predict problem behaviors in children with Autism Spectrum Disorder (ASD) using the new PreMAC framework. This AI tool analyzes precursor signals, offering timely alerts to help manage challenging behaviors effectively.
Area of Science:
- Neurodevelopmental Disorders
- Artificial Intelligence in Healthcare
- Behavioral Science
Background:
- Autism Spectrum Disorder (ASD) affects 1 in 54 US children, with two-thirds exhibiting problem behaviors.
- Predicting and managing problem behaviors is crucial for caregivers of children with ASD.
- Current human prediction methods have limitations, necessitating technological assistance.
Purpose of the Study:
- To propose PreMAC, a machine learning framework for predicting problem behaviors in children with ASD.
- To introduce the M2P3 platform for collecting multimodal data to train PreMAC.
- To assess the feasibility and accuracy of the M2P3 platform and PreMAC.
Main Methods:
- Development of the PreMAC predictive framework using machine learning.
- Design of the M2P3 multimodal data capture platform.
- Integration of the interview-informed synthesized contingency analysis (IISCA) for data collection.
- Feasibility study with seven children (ages 4-15) with ASD.
Main Results:
- The M2P3 platform was well-tolerated by children with ASD.
- PreMAC demonstrated high prediction accuracy for precursors of problem behaviors.
- The framework shows promise for real-time alerts to caregivers.
Conclusions:
- The PreMAC framework and M2P3 platform are feasible for use with children with ASD.
- Machine learning can effectively predict problem behaviors in ASD.
- This technology offers a novel approach to support caregivers in managing challenging behaviors.
Abstract:
Autism Spectrum Disorder (ASD) impacts 1 in 54 children in the US. Two-thirds of children with ASD display problem behavior. If a caregiver can predict that a child is likely to engage in problem behavior, they may be able to take action to minimize that risk. Although experts in Applied Behavior Analysis can offer caregivers recognition and remediation strategies, there are limitations to the extent to which human prediction of problem behavior is possible without the assistance of technology. In this paper, we propose a machine learning-based predictive framework, PreMAC, that uses multimodal signals from precursors of problem behaviors to alert caregivers of impending problem behavior for children with ASD. A multimodal data capture platform, M2P3, was designed to collect multimodal training data for PreMAC. The development of PreMAC integrated a rapid functional analysis, the interview-informed synthesized contingency analysis (IISCA), for collection of training data. A feasibility study with seven 4 to 15-year-old children with ASD was conducted to investigate the tolerability and feasibility of the M2P3 platform and the accuracy of PreMAC. Results indicate that the M2P3 platform was well tolerated by the children and PreMAC could predict precursors of problem behaviors with high prediction accuracies.
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