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

Machine Learning (1501) Autism Spectrum Disorder (ASD) (112) Artificial Intelligence (1421)

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

Journals

  1. Nisar S, Haris M. Neuroimaging genetics approaches to identify new biomarkers for the early diagnosis of autism spectrum disorder. Molecular Psychiatry 2023;28(12):4995 View
  2. Washington P, Wall D. A Review of and Roadmap for Data Science and Machine Learning for the Neuropsychiatric Phenotype of Autism. Annual Review of Biomedical Data Science 2023;6(1):211 View
  3. Kumar A, Jaiswal U. Comparative Analysis of Sentiments in Children with Neurodevelopmental Disorders. ADCAIJ: Advances in Distributed Computing and Artificial Intelligence Journal 2023;12:e31469 View

Books/Policy Documents

  1. Magboo V, Magboo M. Artificial Intelligence and Data Science. View