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

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

Conference Proceedings

  1. Atchison A, Pinto G, Woodward A, Stevens E, Dixon D, Linstead E. 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA). Classifying Challenging Behaviors in Autism Spectrum Disorder with Word Embeddings View