This paper is in the following e-collection/theme issue:

Machine Learning (1400) Autism Spectrum Disorder (ASD) (98) Artificial Intelligence (1214)

Published on in Vol 9, No 6 (2021): June

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/27793, first published .
Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study

Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study

Unsupervised Machine Learning for Identifying Challenging Behavior Profiles to Explore Cluster-Based Treatment Efficacy in Children With Autism Spectrum Disorder: Retrospective Data Analysis Study

Authors of this article:

Julie Gardner-Hoag1 Author Orcid Image ;   Marlena Novack2 Author Orcid Image ;   Chelsea Parlett-Pelleriti3 Author Orcid Image ;   Elizabeth Stevens3 Author Orcid Image ;   Dennis Dixon2 Author Orcid Image ;   Erik Linstead3 Author Orcid Image
Article Authors Cited by (5) Tweetations (10) Metrics

Julie Gardner-Hoag   1 , BSc, MSc ;   Marlena Novack   2 , BA ;   Chelsea Parlett-Pelleriti   3 , BA, MSc ;   Elizabeth Stevens   3 * , BSc, MSc, PhD ;   Dennis Dixon   2 * , BA, PhD ;   Erik Linstead   3 * , BSc, MSc, PhD

1 Schmid College of Science and Technology, Chapman University, Orange, CA, United States

2 Center for Autism and Related Disorders, Woodland Hills, CA, United States

3 Fowler School of Engineering, Chapman University, Orange, CA, United States

*these authors contributed equally

Corresponding Author:

  • Erik Linstead, BSc, MSc, PhD
  • Fowler School of Engineering
  • Chapman University
  • One University Drive
  • Orange, CA, 92866
  • United States
  • Phone: 1 714-289-3159
  • Email: linstead@chapman.edu