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

Overview of attention for article published in Neuropsychiatric Disease and Treatment, June 2015
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  • Average Attention Score compared to outputs of the same age

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2 tweeters
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1 Facebook page

Citations

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14 Dimensions

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61 Mendeley
Title
Outcome predictors in autism spectrum disorders preschoolers undergoing treatment as usual: insights from an observational study using artificial neural networks
Published in
Neuropsychiatric Disease and Treatment, June 2015
DOI 10.2147/ndt.s81233
Pubmed ID
Authors

Antonio Narzisi, Enzo Grossi, Massimo Buscema, Sara Calderoni, Filippo Muratori

Abstract

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. 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. 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". 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.

Twitter Demographics

The data shown below were collected from the profiles of 2 tweeters who shared this research output. Click here to find out more about how the information was compiled.

Mendeley readers

The data shown below were compiled from readership statistics for 61 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 61 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 14 23%
Student > Ph. D. Student 10 16%
Researcher 8 13%
Student > Doctoral Student 6 10%
Student > Postgraduate 4 7%
Other 9 15%
Unknown 10 16%
Readers by discipline Count As %
Psychology 14 23%
Medicine and Dentistry 6 10%
Social Sciences 5 8%
Neuroscience 4 7%
Nursing and Health Professions 4 7%
Other 9 15%
Unknown 19 31%

Attention Score in Context

This research output has an Altmetric Attention Score of 2. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 01 July 2015.
All research outputs
#8,769,238
of 14,535,828 outputs
Outputs from Neuropsychiatric Disease and Treatment
#1,289
of 2,442 outputs
Outputs of similar age
#113,994
of 233,334 outputs
Outputs of similar age from Neuropsychiatric Disease and Treatment
#77
of 101 outputs
Altmetric has tracked 14,535,828 research outputs across all sources so far. This one is in the 37th percentile – i.e., 37% of other outputs scored the same or lower than it.
So far Altmetric has tracked 2,442 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 6.0. This one is in the 39th percentile – i.e., 39% of its peers scored the same or lower than it.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 233,334 tracked outputs that were published within six weeks on either side of this one in any source. This one is in the 47th percentile – i.e., 47% of its contemporaries scored the same or lower than it.
We're also able to compare this research output to 101 others from the same source and published within six weeks on either side of this one. This one is in the 11th percentile – i.e., 11% of its contemporaries scored the same or lower than it.