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A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT) / K. K. MUJEEB RAHMAN in Journal of Autism and Developmental Disorders, 52-6 (June 2022)
[article]
Titre : A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT) Type de document : Texte imprimé et/ou numérique Auteurs : K. K. MUJEEB RAHMAN, Auteur ; M. MONICA SUBASHINI, Auteur Article en page(s) : p.2732-2746 Langues : Anglais (eng) Mots-clés : Auc Autism spectrum disorder Deep neural networks (DNN) Machine learning Qchat Qchat-10 Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) is an abnormal condition of brain development characterized by impaired cognitive ability, speech and human interactions, in addition to a set of repetitive and stereotyped patterns of behaviours. Although no cure for autism exists, early medical intervention can improve the associated symptoms and quality of life. Several manually executed screening tools help to identify the ASD-related behavioural traits in the children that assists the specialist in diagnosing the disease accurately. The quantitative checklist for autism in toddlers (QCHAT) is one of the efficient screening tools used worldwide for ASD screening. ASD diagnosis requires many different manually administered procedures; hence long delay is encountered in getting final results. In recent years, deep neural network (DNN) popularity has been immensely increasing due to its supremacy in solving complex problems. The objective of this research is to apply algorithms, based on the deep neural network (DNN) to identify patients with ASD from the QCHAT datasets. We have used two datasets, the QCHAT and QCHAT-10, in our study. The results obtained show that related to contemporary techniques, the proposed method brings better performance. En ligne : http://dx.doi.org/10.1007/s10803-021-05141-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=474
in Journal of Autism and Developmental Disorders > 52-6 (June 2022) . - p.2732-2746[article] A Deep Neural Network-Based Model for Screening Autism Spectrum Disorder Using the Quantitative Checklist for Autism in Toddlers (QCHAT) [Texte imprimé et/ou numérique] / K. K. MUJEEB RAHMAN, Auteur ; M. MONICA SUBASHINI, Auteur . - p.2732-2746.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 52-6 (June 2022) . - p.2732-2746
Mots-clés : Auc Autism spectrum disorder Deep neural networks (DNN) Machine learning Qchat Qchat-10 Index. décimale : PER Périodiques Résumé : Autism spectrum disorder (ASD) is an abnormal condition of brain development characterized by impaired cognitive ability, speech and human interactions, in addition to a set of repetitive and stereotyped patterns of behaviours. Although no cure for autism exists, early medical intervention can improve the associated symptoms and quality of life. Several manually executed screening tools help to identify the ASD-related behavioural traits in the children that assists the specialist in diagnosing the disease accurately. The quantitative checklist for autism in toddlers (QCHAT) is one of the efficient screening tools used worldwide for ASD screening. ASD diagnosis requires many different manually administered procedures; hence long delay is encountered in getting final results. In recent years, deep neural network (DNN) popularity has been immensely increasing due to its supremacy in solving complex problems. The objective of this research is to apply algorithms, based on the deep neural network (DNN) to identify patients with ASD from the QCHAT datasets. We have used two datasets, the QCHAT and QCHAT-10, in our study. The results obtained show that related to contemporary techniques, the proposed method brings better performance. En ligne : http://dx.doi.org/10.1007/s10803-021-05141-2 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=474 Utility of the Asperger Syndrome Diagnostic Scale in the Assessment of Autism Spectrum Disorders / Amy CAMODECA in Journal of Autism and Developmental Disorders, 50-2 (February 2020)
[article]
Titre : Utility of the Asperger Syndrome Diagnostic Scale in the Assessment of Autism Spectrum Disorders Type de document : Texte imprimé et/ou numérique Auteurs : Amy CAMODECA, Auteur ; Kylie Q. TODD, Auteur ; Jennifer CROYLE, Auteur Article en page(s) : p.513-523 Langues : Anglais (eng) Mots-clés : Auc Autism Criterion validity Questionnaires Roc Reliability Index. décimale : PER Périodiques Résumé : Investigated internal consistency reliability and criterion validity of the Asperger Syndrome Diagnostic Scale (ASDS) in a well-characterized sample of 120 children ([Formula: see text] = 9.91; autism [AUT] n = 54; non-autism [NOT] n = 66) who completed comprehensive outpatient evaluations with a gold-standard measure, the Autism Diagnostic Observation Schedule-2. With the exception of a low Cognitive alpha in the AUT group, internal consistency reliabilities ranged from moderate to high. Significant between-group mean differences were observed for all scores. Receiver operating characteristic analyses indicated Area Under the Curve in the fair range (.71). Cutoff points and interpretation are discussed. The ASDS appears most useful in cases of either low or high scores or as an adjuvant to gold-standard measures. En ligne : http://dx.doi.org/10.1007/s10803-019-04272-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=416
in Journal of Autism and Developmental Disorders > 50-2 (February 2020) . - p.513-523[article] Utility of the Asperger Syndrome Diagnostic Scale in the Assessment of Autism Spectrum Disorders [Texte imprimé et/ou numérique] / Amy CAMODECA, Auteur ; Kylie Q. TODD, Auteur ; Jennifer CROYLE, Auteur . - p.513-523.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 50-2 (February 2020) . - p.513-523
Mots-clés : Auc Autism Criterion validity Questionnaires Roc Reliability Index. décimale : PER Périodiques Résumé : Investigated internal consistency reliability and criterion validity of the Asperger Syndrome Diagnostic Scale (ASDS) in a well-characterized sample of 120 children ([Formula: see text] = 9.91; autism [AUT] n = 54; non-autism [NOT] n = 66) who completed comprehensive outpatient evaluations with a gold-standard measure, the Autism Diagnostic Observation Schedule-2. With the exception of a low Cognitive alpha in the AUT group, internal consistency reliabilities ranged from moderate to high. Significant between-group mean differences were observed for all scores. Receiver operating characteristic analyses indicated Area Under the Curve in the fair range (.71). Cutoff points and interpretation are discussed. The ASDS appears most useful in cases of either low or high scores or as an adjuvant to gold-standard measures. En ligne : http://dx.doi.org/10.1007/s10803-019-04272-x Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=416