Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
The work presented here is a part of an ongoing study at PRaDA.
References
Diagnostic and statistical manual of mental disorders, fifth edn. American Psychiatric Association, May 2013
Verte, S., Geurts, H.M., Roeyers, H., Oosterlaan, J., Sergeant, J.A.: Executive functioning in children with an autism spectrum disorder: can we differentiate within the spectrum? J. Autism Dev. Disord. 36, 351–372 (2006)
Garnett, M.S., Attwood, T., Peterson, C., Kelly, A.B.: Autism spectrum conditions among children and adolescents: a new profiling tool. Aust. J. Psychol. 65, 206–213 (2013)
Munson, J., Dawson, G., Sterling, L., Beauchaine, T., Zhou, A., Koehler, E., Lord, C., Rogers, S., Sigman, M., Estes, A., et al.: Evidence for latent classes of iq in young children with autism spectrum disorder. J. Inf. 113, 439–452 (2008)
Williams, J.G., Allison, C., Scott, F.J., Bolton, P.F., Baron-Cohen, S., Matthews, F.E., Brayne, C.: The childhood autism spectrum test (cast): sex differences. J. Autism Dev. Disord. 38, 1731–1739 (2008)
Bartley, J.J.: An update on autism: science, gender, and the law. Gend. Med. 3, 73–78 (2006)
Dworzynski, K., Ronald, A., Bolton, P., Happé, F.: How different are girls and boys above and below the diagnostic threshold for autism spectrum disorders? J. Am. Acad. Child Adolesc. Psychiatry 51, 788–797 (2012)
Dawson, G., et al.: Early behavioral intervention, brain plasticity, and the prevention of autism spectrum disorder. Dev. Psychopathol. 20, 775 (2008)
Fenske, E.C., Zalenski, S., Krantz, P.J., McClannahan, L.E.: Age at intervention and treatment outcome for autistic children in a comprehensive intervention program. Anal. Interv. Dev. Disabil. 5, 49–58 (1985)
Venkatesh, S., Phung, D., Duong, T., Greenhill, S., Adams, B.: Toby: early intervention in autism through technology. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 3187–3196. ACM (2013)
Vellanki, P., Duong, T., Venkatesh, S., Phung, D.: Nonparametric discovery of learning patterns and autism subgroups from therapeutic data. In: Proceedings of 22nd International Conference on Pattern Recognition (ICPR), pp. 1829–1833 (2014)
Venkatesh, S., Greenhill, S., Phung, D., Adams, B., Duong, T.: Pervasive multimedia for autism intervention. Pervasive Mob. Comput. 8, 863–882 (2012)
Moore, D., Venkatesh, S., Anderson, A., Greenhill, S., Phung, D., Duong, T., Cairns, D., Marshall, W., Whitehouse, A.: Toby play-pad application to teach children with asd: a pilot trial. Dev. Neurorehabil. 18, 213–217 (2013)
Howard, K.I., Moras, K., Brill, P.L., Martinovich, Z., Lutz, W.: Evaluation of psychotherapy: efficacy, effectiveness, and patient progress. Am. Psychol. 51, 1059 (1996)
Mazalan, L., Halim, N.M., Omar, H.A., Zaini, N.M.: Profiling system for depressive disorder patient using web based approaches, pp. 207–212 (2012)
Payne-Murphy, J.C., Beacham, A.O.: Revisiting chronic pain patient profiling: an acceptance-based approach in an online sample. Clin. Psychol. Psychother 22, 240–248 (2014)
Nguyen, T.D., Tran, T., Phung, D., Venkatesh, S.: Latent patient profile modelling and applications with mixed-variate restricted Boltzmann machine. In: Pei, J., Tseng, V.S., Cao, L., Motoda, H., Xu, G. (eds.) PAKDD 2013, Part I. LNCS, vol. 7818, pp. 123–135. Springer, Heidelberg (2013)
Tran, T., Phung, D., Venkatesh, S.: Mixed-variate restricted Boltzmann machines. In: ACML 2011 Proceedings of the 3rd Asian Conference on Machine Learning, [JMLR], pp. 213–229 (2011)
Van der Maaten, L., Hinton, G.: Visualizing data using t-sne. J. Mach. Learn. Res. 9, 85 (2008)
Hinton, G.E., Salakhutdinov, R.: Replicated softmax: an undirected topic model. In: Advances in Neural Information Processing Systems, pp. 1607–1614 (2009)
Hinton, G.E.: Training products of experts by minimizing contrastive divergence. Neural Comput. 14, 1771–1800 (2002)
Hinton, G., Osindero, S., Teh, Y.W.: A fast learning algorithm for deep belief nets. Neural Comput. 18, 1527–1554 (2006)
Salakhutdinov, R., Hinton, G.E.: Deep Boltzmann machines. In: International Conference on Artificial Intelligence and Statistics, pp. 448–455 (2009)
Gregor, K., Danihelka, I., Mnih, A., Blundell, C., Wierstra, D.: Deep autoregressive networks (2013). arXiv preprint arXiv:1310.8499
Wainwright, M.J., Jordan, M.I.: Graphical models, exponential families, and variational inference. Found. Trends Mach. Learn. 1, 1–305 (2008)
Hinton, G.: A practical guide to training restricted Boltzmann machines. Momentum 9, 926 (2010)
Salakhutdinov, R., Mnih, A., Hinton, G.: Restricted Boltzmann machines for collaborative filtering. In: Proceedings of the 24th International Conference on Machine Learning, pp. 791–798. ACM (2007)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2015 Springer International Publishing Switzerland
About this paper
Cite this paper
Vellanki, P., Phung, D., Duong, T., Venkatesh, S. (2015). Learning Entry Profiles of Children with Autism from Multivariate Treatment Information Using Restricted Boltzmann Machines. In: Li, XL., Cao, T., Lim, EP., Zhou, ZH., Ho, TB., Cheung, D. (eds) Trends and Applications in Knowledge Discovery and Data Mining. Lecture Notes in Computer Science(), vol 9441. Springer, Cham. https://doi.org/10.1007/978-3-319-25660-3_21
Download citation
DOI: https://doi.org/10.1007/978-3-319-25660-3_21
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25659-7
Online ISBN: 978-3-319-25660-3
eBook Packages: Computer ScienceComputer Science (R0)