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

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

Antonio Narzisi,1 Filippo Muratori,1,2 Massimo Buscema,3,4 Sara Calderoni,1 Enzo Grossi3,5 1Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, 2Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy; 3Semeion Research Centre of Sciences of Communicat...

Descripción completa

Guardado en:
Detalles Bibliográficos
Autores principales: Narzisi A, Muratori F, Buscema M, Calderoni S, Grossi E
Formato: article
Lenguaje:EN
Publicado: Dove Medical Press 2015
Materias:
Neurosciences. Biological psychiatry. Neuropsychiatry
RC321-571
Neurology. Diseases of the nervous system
RC346-429
Acceso en línea:https://doaj.org/article/0d904b89b679448ab3075afaaefe1c0d
Etiquetas: Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
id oai:doaj.org-article:0d904b89b679448ab3075afaaefe1c0d
record_format dspace
spelling oai:doaj.org-article:0d904b89b679448ab3075afaaefe1c0d2021-12-02T04:19:37ZOutcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks1178-2021https://doaj.org/article/0d904b89b679448ab3075afaaefe1c0d2015-06-01T00:00:00Zhttp://www.dovepress.com/outcome-predictors-in-autism-spectrum-disorders-preschoolers-undergoin-peer-reviewed-article-NDThttps://doaj.org/toc/1178-2021Antonio Narzisi,1 Filippo Muratori,1,2 Massimo Buscema,3,4 Sara Calderoni,1 Enzo Grossi3,5 1Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, 2Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy; 3Semeion Research Centre of Sciences of Communication, Rome, Italy; 4Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA; 5Autism Research Unit, Villa Santa Maria Institute, Tavernerio, Italy 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. Keywords: autism spectrum disorders, treatment, intervention, artificial neural networks, outcomeNarzisi AMuratori FBuscema MCalderoni SGrossi EDove Medical PressarticleNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571Neurology. Diseases of the nervous systemRC346-429ENNeuropsychiatric Disease and Treatment, Vol 2015, Iss default, Pp 1587-1599 (2015)
institution DOAJ
collection DOAJ
language EN
topic Neurosciences. Biological psychiatry. Neuropsychiatry
RC321-571
Neurology. Diseases of the nervous system
RC346-429
spellingShingle Neurosciences. Biological psychiatry. Neuropsychiatry
RC321-571
Neurology. Diseases of the nervous system
RC346-429
Narzisi A
Muratori F
Buscema M
Calderoni S
Grossi E
Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks
description Antonio Narzisi,1 Filippo Muratori,1,2 Massimo Buscema,3,4 Sara Calderoni,1 Enzo Grossi3,5 1Department of Developmental Neuroscience, IRCCS Stella Maris Foundation, 2Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy; 3Semeion Research Centre of Sciences of Communication, Rome, Italy; 4Department of Mathematical and Statistical Sciences, University of Colorado Denver, Denver, CO, USA; 5Autism Research Unit, Villa Santa Maria Institute, Tavernerio, Italy 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. Keywords: autism spectrum disorders, treatment, intervention, artificial neural networks, outcome
format article
author Narzisi A
Muratori F
Buscema M
Calderoni S
Grossi E
author_facet Narzisi A
Muratori F
Buscema M
Calderoni S
Grossi E
author_sort Narzisi A
title 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_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_sort outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks
publisher Dove Medical Press
publishDate 2015
url https://doaj.org/article/0d904b89b679448ab3075afaaefe1c0d
work_keys_str_mv AT narzisia outcomepredictorsinautismspectrumdisorderspreschoolersundergoingtreatmentasnbspusualinsightsfromanobservationalstudyusingartificialneuralnetworks
AT muratorif outcomepredictorsinautismspectrumdisorderspreschoolersundergoingtreatmentasnbspusualinsightsfromanobservationalstudyusingartificialneuralnetworks
AT buscemam outcomepredictorsinautismspectrumdisorderspreschoolersundergoingtreatmentasnbspusualinsightsfromanobservationalstudyusingartificialneuralnetworks
AT calderonis outcomepredictorsinautismspectrumdisorderspreschoolersundergoingtreatmentasnbspusualinsightsfromanobservationalstudyusingartificialneuralnetworks
AT grossie outcomepredictorsinautismspectrumdisorderspreschoolersundergoingtreatmentasnbspusualinsightsfromanobservationalstudyusingartificialneuralnetworks
_version_ 1718401292594839552

Ejemplares similares