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](https://asset.jmir.pub/assets/0c5a03097e1d67452181f7a56c4726de.png 480w,https://asset.jmir.pub/assets/0c5a03097e1d67452181f7a56c4726de.png 960w,https://asset.jmir.pub/assets/0c5a03097e1d67452181f7a56c4726de.png 1920w,https://asset.jmir.pub/assets/0c5a03097e1d67452181f7a56c4726de.png 2500w)
Journals
- 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
- 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
- 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
- Magboo V, Magboo M. Artificial Intelligence and Data Science. View