Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities / Alessandro CRIPPA in Journal of Autism and Developmental Disorders, 45-7 (July 2015)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 45-7 (July 2015) . - p.2146-2156 Titre : Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities Type de document : Texte imprimé et/ou numérique Auteurs : Alessandro CRIPPA, Auteur ; Christian SALVATORE, Auteur ; Paolo PEREGO, Auteur ; Sara FORTI, Auteur ; Maria NOBILE, Auteur ; Massimo MOLTENI, Auteur ; Isabella CASTIGLIONI, Auteur Année de publication : 2015 Article en page(s) : p.2146-2156 Langues : Anglais (eng) Mots-clés : Autism spectrum disorder  Kinematics  Classification  Machine learning  Support vector machines Index. décimale : PER Périodiques Résumé : In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2–4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children with ASD from 15 typically developing children by means of kinematic analysis of a simple reach-to-drop task. Our method reached a maximum classification accuracy of 96.7 % with seven features related to the goal-oriented part of the movement. These preliminary findings offer insight into a possible motor signature of ASD that may be potentially useful in identifying a well-defined subset of patients, reducing the clinical heterogeneity within the broad behavioral phenotype. En ligne : http://dx.doi.org/10.1007/s10803-015-2379-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=261 [article] Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities [Texte imprimé et/ou numérique] / Alessandro CRIPPA, Auteur ; Christian SALVATORE, Auteur ; Paolo PEREGO, Auteur ; Sara FORTI, Auteur ; Maria NOBILE, Auteur ; Massimo MOLTENI, Auteur ; Isabella CASTIGLIONI, Auteur . - 2015 . - p.2146-2156. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 45-7 (July 2015) . - p.2146-2156 Mots-clés : Autism spectrum disorder  Kinematics  Classification  Machine learning  Support vector machines Index. décimale : PER Périodiques Résumé : In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2–4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children with ASD from 15 typically developing children by means of kinematic analysis of a simple reach-to-drop task. Our method reached a maximum classification accuracy of 96.7 % with seven features related to the goal-oriented part of the movement. These preliminary findings offer insight into a possible motor signature of ASD that may be potentially useful in identifying a well-defined subset of patients, reducing the clinical heterogeneity within the broad behavioral phenotype. En ligne : http://dx.doi.org/10.1007/s10803-015-2379-8 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=261

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