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Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks

BACKGROUND: Treatment as usual (TAU) for autism spectrum disorders (ASDs) includes eclectic treatments usually available in the community and school inclusion with an individual support teacher. Artificial neural networks (ANNs) have never been used to study the effects of treatment in ASDs. The Aut...

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Autores principales: Narzisi, Antonio, Muratori, Filippo, Buscema, Massimo, Calderoni, Sara, Grossi, Enzo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2015
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4494609/
https://www.ncbi.nlm.nih.gov/pubmed/26170671
http://dx.doi.org/10.2147/NDT.S81233
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author Narzisi, Antonio
Muratori, Filippo
Buscema, Massimo
Calderoni, Sara
Grossi, Enzo
author_facet Narzisi, Antonio
Muratori, Filippo
Buscema, Massimo
Calderoni, Sara
Grossi, Enzo
author_sort Narzisi, Antonio
collection PubMed
description BACKGROUND: Treatment as usual (TAU) for autism spectrum disorders (ASDs) includes eclectic treatments usually available in the community and school inclusion with an individual support teacher. Artificial neural networks (ANNs) have never been used to study the effects of treatment in ASDs. The Auto Contractive Map (Auto-CM) is a kind of ANN able to discover trends and associations among variables creating a semantic connectivity map. The matrix of connections, visualized through a minimum spanning tree filter, takes into account nonlinear associations among variables and captures connection schemes among clusters. Our aim is to use Auto-CM to recognize variables to discriminate between responders versus no responders at TAU. METHODS: A total of 56 preschoolers with ASDs were recruited at different sites in Italy. They were evaluated at T0 and after 6 months of treatment (T1). The children were referred to community providers for usual treatments. RESULTS: At T1, the severity of autism measured through the Autism Diagnostic Observation Schedule decreased in 62% of involved children (Response), whereas it was the same or worse in 37% of the children (No Response). The application of the Semeion ANNs overcomes the 85% of global accuracy (Sine Net almost reaching 90%). Consequently, some of the tested algorithms were able to find a good correlation between some variables and TAU outcome. The semantic connectivity map obtained with the application of the Auto-CM system showed results that clearly indicated that “Response” cases can be visually separated from the “No Response” cases. It was possible to visualize a response area characterized by “Parents Involvement high”. The resultant No Response area strongly connected with “Parents Involvement low”. CONCLUSION: The ANN model used in this study seems to be a promising tool for the identification of the variables involved in the positive response to TAU in autism.
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spelling pubmed-44946092015-07-13 Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks Narzisi, Antonio Muratori, Filippo Buscema, Massimo Calderoni, Sara Grossi, Enzo Neuropsychiatr Dis Treat Original Research BACKGROUND: Treatment as usual (TAU) for autism spectrum disorders (ASDs) includes eclectic treatments usually available in the community and school inclusion with an individual support teacher. Artificial neural networks (ANNs) have never been used to study the effects of treatment in ASDs. The Auto Contractive Map (Auto-CM) is a kind of ANN able to discover trends and associations among variables creating a semantic connectivity map. The matrix of connections, visualized through a minimum spanning tree filter, takes into account nonlinear associations among variables and captures connection schemes among clusters. Our aim is to use Auto-CM to recognize variables to discriminate between responders versus no responders at TAU. METHODS: A total of 56 preschoolers with ASDs were recruited at different sites in Italy. They were evaluated at T0 and after 6 months of treatment (T1). The children were referred to community providers for usual treatments. RESULTS: At T1, the severity of autism measured through the Autism Diagnostic Observation Schedule decreased in 62% of involved children (Response), whereas it was the same or worse in 37% of the children (No Response). The application of the Semeion ANNs overcomes the 85% of global accuracy (Sine Net almost reaching 90%). Consequently, some of the tested algorithms were able to find a good correlation between some variables and TAU outcome. The semantic connectivity map obtained with the application of the Auto-CM system showed results that clearly indicated that “Response” cases can be visually separated from the “No Response” cases. It was possible to visualize a response area characterized by “Parents Involvement high”. The resultant No Response area strongly connected with “Parents Involvement low”. CONCLUSION: The ANN model used in this study seems to be a promising tool for the identification of the variables involved in the positive response to TAU in autism. Dove Medical Press 2015-06-30 /pmc/articles/PMC4494609/ /pubmed/26170671 http://dx.doi.org/10.2147/NDT.S81233 Text en © 2015 Narzisi et al. This work is published by Dove Medical Press Limited, and licensed under Creative Commons Attribution – Non Commercial (unported, v3.0) License The full terms of the License are available at http://creativecommons.org/licenses/by-nc/3.0/. Non-commercial uses of the work are permitted without any further permission from Dove Medical Press Limited, provided the work is properly attributed.
spellingShingle Original Research
Narzisi, Antonio
Muratori, Filippo
Buscema, Massimo
Calderoni, Sara
Grossi, Enzo
Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks
title Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks
title_full Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks
title_fullStr Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks
title_full_unstemmed Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks
title_short Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks
title_sort outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4494609/
https://www.ncbi.nlm.nih.gov/pubmed/26170671
http://dx.doi.org/10.2147/NDT.S81233
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