Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities.
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...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 45; no. 7; pp. 2146 - 2157 |
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| Autores principales: | , , , , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Springer Nature
Jul2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109802867&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109802867 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: Jul2015 vid: 45 iid: 7 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 109802867 103287233 10.1007/s10803-015-2379-8 NLM25652603 109802867 ppf: 2146 ppct: 11 formats: fmt: @attributes: type: P tig: atl: Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities. aug: au: Crippa, Alessandro Salvatore, Christian Perego, Paolo Forti, Sara Nobile, Maria Molteni, Massimo Castiglioni, Isabella affil: Institute of Molecular Imaging and Physiology, National Research Council, Via F.lli Cervi 93 20090 Segrate Italy sug: subj: Autism Spectrum Disorder Diagnosis Autism Spectrum Disorder Pathology Motor Skills Disorders Diagnosis Autism Spectrum Disorder Complications Machinery Human Descriptive Statistics Upper Extremity Movement Kinematics Scales Analysis of Covariance Algorithms Sensitivity and Specificity Male Female Child, Preschool Chi Square Test Validity Data Analysis Software Funding Source Child, Preschool: 2-5 years Male Female ab: 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. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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