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Behavioral approaches have often been criticized for ignoring mental processes and focusing solely on observable behavior. However, these approaches provide an optimistic perspective for individuals seeking to change their behaviors. Rather than concentrating on intrinsic personality traits, behavioral approaches suggest that even longstanding habits can be modified by changing the reward contingencies that maintain them.
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Machine learning-based ABA treatment recommendation and personalization for autism spectrum disorder: an exploratory

Manu Kohli1, Arpan Kumar Kar2, Anjali Bangalore3

  • 1Indian Institute of Technology-Delhi, Department of Management Studies, IV Floor, Vishwakarma Bhavan, Shaheed Jeet Singh Marg, Hauz Khas, New Delhi, 110016, India. manu.kohli@dms.iitd.ac.in.

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Summary

Machine learning algorithms can personalize autism spectrum disorder (ASD) treatment goals, improving upon traditional methods. This approach addresses shortages in applied behavior analysis (ABA) practitioners and enhances data-driven decision-making for better outcomes.

Keywords:
ABAASDAutismCollaborative filteringMachine learningPatient similarity

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Developmental Psychology

Background:

  • Autism spectrum disorder (ASD) is a neurodevelopmental condition impacting social communication and behavior.
  • Applied Behavior Analysis (ABA) is a primary treatment for ASD, but faces challenges.
  • Shortages of ABA practitioners and clinician subjectivity hinder effective, data-driven treatment planning.

Purpose of the Study:

  • To develop and evaluate machine learning algorithms for recommending and personalizing ABA treatment goals for individuals with ASD.
  • To address limitations in current ABA practice, including practitioner shortages and subjective decision-making.

Main Methods:

  • Two machine learning algorithms (patient similarity and collaborative filtering) were applied to personalize ABA treatment goals.
  • The models predicted ABA treatment recommendations for 29 participants with ASD.
  • Treatment efficacy was assessed by the percentage of mastered goals.

Main Results:

  • Machine learning models achieved 81-84% accuracy in predicting ABA treatment goals.
  • Normalized Discounted Cumulative Gain (NDCG) scores ranged from 79-81%, indicating strong performance compared to clinician recommendations.
  • Treatment efficacy varied, with the percentage of mastered goals indicating the success of recommended interventions.

Conclusions:

  • Machine learning offers a viable solution to personalize ABA treatment goals for ASD, overcoming current practice limitations.
  • The proposed strategy demonstrates generalizability to other interventions and neurological disorders.
  • This data-driven approach can enhance the quality and accessibility of ASD interventions.