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Machine learning determination of applied behavioral analysis treatment plan type.

Jenish Maharjan1, Anurag Garikipati1, Frank A Dinenno1

  • 1Montera Inc. dba Forta, 548 Market St, San Francisco, CA, PMB 89605, USA.

Brain Informatics
|March 2, 2023
PubMed
Summary

A machine learning model accurately predicts the best Applied Behavioral Analysis (ABA) treatment intensity for autism spectrum disorder (ASD) patients. This tool can help standardize treatment decisions and improve resource allocation for individuals with ASD.

Keywords:
Applied behavioral analysisArtificial intelligenceAutism spectrum disorderMachine learning

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Area of Science:

  • Machine Learning in Healthcare
  • Behavioral Science
  • Autism Spectrum Disorder Research

Background:

  • Applied Behavioral Analysis (ABA) is a leading treatment for autism spectrum disorder (ASD), with varying intensities: comprehensive (20-40 hrs/week) and focused (10-20 hrs/week).
  • Current methods for determining ABA treatment intensity are subjective and lack standardization, potentially impacting patient outcomes.
  • This study explored the use of a machine learning (ML) model to objectively classify optimal ABA treatment intensity for ASD patients.

Purpose of the Study:

  • To evaluate an ML prediction model's ability to classify appropriate ABA treatment intensity (comprehensive vs. focused) for individuals with ASD.
  • To compare the ML model's performance against the current standard of care in determining ABA treatment intensity.
  • To identify key patient data features that contribute to accurate treatment intensity predictions.

Main Methods:

  • A retrospective analysis of data from 359 ASD patients was conducted to train and test an XGBoost ML model.
  • Input data included patient demographics, schooling, behavior, skills, and goals.
  • Model performance was evaluated using AUROC, sensitivity, specificity, PPV, and NPV, and compared to Behavior Analyst Certification Board guidelines.

Main Results:

  • The ML model demonstrated high performance in classifying treatment intensity (AUROC: 0.895), significantly outperforming the standard of care (AUROC: 0.767).
  • The model achieved a sensitivity of 0.789 and specificity of 0.808, with only 14 misclassifications out of 71 test cases.
  • Key features influencing predictions included bathing ability, age, and previous ABA treatment hours.

Conclusions:

  • The developed ML model effectively classifies appropriate ABA treatment intensity using accessible patient data.
  • This approach has the potential to standardize ABA treatment intensity determination for ASD patients.
  • Implementing this model could facilitate timely initiation of optimal treatment and enhance resource allocation in ASD care.