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Learning entry profiles of children with autism from multivariate treatment information using restricted boltzmann machines

Vellanki, Pratibha, Phung, Dinh, Duong, Thi and Venkatesh, Svetha 2015, Learning entry profiles of children with autism from multivariate treatment information using restricted boltzmann machines. In Li, Xiao-Li, Cao, Tru, Lim, Ee-Peng, Zhou, Zhi-Hua, Ho, Tu-Bao, Cheung, David and Motoda, Hiroshi (ed), 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 Papers, Springer, Berlin, Germany, pp.245-257, doi: 10.1007/978-3-319-25660-3_21.

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Title Learning entry profiles of children with autism from multivariate treatment information using restricted boltzmann machines
Author(s) Vellanki, Pratibha
Phung, DinhORCID iD for Phung, Dinh orcid.org/0000-0002-9977-8247
Duong, Thi
Venkatesh, SvethaORCID iD for Venkatesh, Svetha orcid.org/0000-0001-8675-6631
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 Papers
Editor(s) Li, Xiao-Li
Cao, Tru
Lim, Ee-Peng
Zhou, Zhi-Hua
Ho, Tu-Bao
Cheung, David
Motoda, Hiroshi
Publication date 2015
Series Lecture notes in artifical intelligence; v.9441
Chapter number 21
Total chapters 23
Start page 245
End page 257
Total pages 13
Publisher Springer
Place of Publication Berlin, Germany
Keyword(s) Science & Technology
Technology
Computer Science, Artificial Intelligence
Computer Science
SPECTRUM DISORDER
INTERVENTION
Summary Entry 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.
ISBN 9783319256603
ISSN 0302-9743
1611-3349
Language eng
DOI 10.1007/978-3-319-25660-3_21
Field of Research 080109 Pattern Recognition and Data Mining
Socio Economic Objective 970108 Expanding Knowledge in the Information and Computing Sciences
HERDC Research category B1 Book chapter
ERA Research output type B Book chapter
Copyright notice ©2015, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082623

Document type: Book Chapter
Collections: Centre for Pattern Recognition and Data Analytics
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