Name
Title | ||
|---|---|---|
Identification and analysis of behavioral phenotypes in autism spectrum disorder via unsupervised machine learning |
Abstract | ||
|---|---|---|
The high amount of variance explained by the regression models indicates that clustering provides a basis for homogenization, and thus an opportunity to tailor treatment based on cluster memberships. These findings have significant implications on prognosis and targeted treatment of ASD, and pave the way for personalized intervention based on unsupervised machine learning. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1016/j.ijmedinf.2019.05.006 | International Journal of Medical Informatics |
Keywords | Field | DocType |
Machine learning,Autism spectrum disorder,Behavioral phenotypes,Cluster analysis,Treatment response | Hierarchical clustering,Data mining,Regression,Regression analysis,Unsupervised learning,Computational biology,Autism spectrum disorder,Cluster analysis,Explained variation,Medicine,Mixture model | Journal |
Volume | ISSN | Citations |
129 | 1386-5056 | 2 |
PageRank | References | Authors |
0.37 | 0 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Elizabeth Stevens | 1 | 5 | 0.74 |
| Dennis R. Dixon | 2 | 2 | 0.37 |
| Marlena N. Novack | 3 | 2 | 0.37 |
| Doreen Granpeesheh | 4 | 3 | 0.74 |
| Tristram Smith | 5 | 2 | 0.37 |
| Erik Linstead | 6 | 360 | 27.44 |