Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities (Articolo in rivista)

Type
Label
  • Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities (Articolo in rivista) (literal)
Anno
  • 2015-01-01T00:00:00+01:00 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#doi
  • 10.1007/s10803-015-2379-8 (literal)
Alternative label
  • Alessandro Crippa 1,2 Christian Salvatore 2, Paolo Perego 3, Sara Forti 1, Maria Nobile 1,4, Massimo Molteni 1, Isabella Castiglioni 2 (2015)
    Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities
    in Journal of autism and developmental disorders
    (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#autori
  • Alessandro Crippa 1,2 Christian Salvatore 2, Paolo Perego 3, Sara Forti 1, Maria Nobile 1,4, Massimo Molteni 1, Isabella Castiglioni 2 (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#altreInformazioni
  • Pubblicato on-line: 5 Febbraio 2015 (literal)
Rivista
Note
  • PubMe (literal)
Http://www.cnr.it/ontology/cnr/pubblicazioni.owl#affiliazioni
  • 1. Child Psychopathology Unit, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Lecco, Italy 2. Institute of Molecular Imaging and Physiology, National Research Council, Segrate, Milan, Italy 3. Bioengineering Lab, Scientific Institute, IRCCS Eugenio Medea, Bosisio Parini, Lecco, Italy 4. Department of Clinical Neurosciences, Hermanas Hospitalarias, FoRiPsi, Albese con Cassano, Italy (literal)
Titolo
  • Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities (literal)
Abstract
  • 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. (literal)
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