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Learning entry profiles of children with autism from multivariate treatment information using restricted boltzmann machines
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posted on 2015-11-26, 00:00 authored by Pratibha Vellanki, Quoc-Dinh Phung, Thi Duong, Svetha VenkateshSvetha VenkateshEntry profiles can be generated before children with Autism Spectrum Disorders (ASD) begin to traverse an intervention program. They can help evaluate the progress of each child on the dedicated syllabus in addition to enabling narrowing down the best intervention course over time. However, the traits of ASD are expressed in different ways in every individual affected. The resulting spectrum nature of the disorder makes it challenging to discover profiles of children with ASD. Using data from 491 children, traversing the syllabus of a comprehensive intervention program on iPad called TOBY Playpad, we learn the entry profiles of the children based on their age, sex and performance on their first skills of the syllabus. Mixed-variate restricted Boltzmann machines allow us to integrate the heterogeneous data into one model making it a suitable technique. The data based discovery of entry profiles may assist in developing systems that can automatically suggest best suitable paths through the syllabus by clustering the children based on the characteristics they present at the beginning of the program. This may open the pathway for personalised intervention.
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Title of book
Trends and applications in knowledge discovery and data mining: PAKDD 2015 Workshops: BigPMA, VLSP, QIMIE, DAEBH Ho Chi Minh City, Vietnam, May 19–21, 2015 Revised Selected PapersVolume
9441Series
Lecture notes in artifical intelligence; v.9441Chapter number
21Pagination
245 - 257Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
0302-9743eISSN
1611-3349ISBN-13
9783319256603Language
engPublication classification
B Book chapter; B1 Book chapterCopyright notice
2015, SpringerExtent
23Editor/Contributor(s)
X Li, T Cao, E Lim, Z Zhou, T Ho, D Cheung, H MotodaUsage metrics
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