A neural NETWORK approach to the classification of autism.
A nonlinear pattern recognition system, neural network technology, was explored for its utility in assisting in the classification of autism. It was compared with a more traditional approach, simultaneous and stepwise linear discriminant analyses, in terms of the ability of each methodology to both...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 23; no. 3; pp. 443 - 467 |
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| Autores principales: | , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Sep1993
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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=106099911&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 106099911 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: Sep1993 vid: 23 iid: 3 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 106099911 106099911 2009458983 10.1007/bf01046050 NLM8226581 106099911 ppf: 443 ppct: 24 formats: fmt: @attributes: type: P tig: atl: A neural NETWORK approach to the classification of autism. aug: au: Cohen IL Sudhalter V sug: subj: Autism Spectrum Disorder Classification Neural Networks (Computer) Adolescence Autism Spectrum Disorder Diagnosis Child Diagnosis, Computer Assisted Discriminant Analysis Female Male Intellectual Disability Classification Intellectual Disability Diagnosis Psychological Tests Reproducibility of Results Human Adolescent: 13-18 years Child: 6-12 years Female Male ab: A nonlinear pattern recognition system, neural network technology, was explored for its utility in assisting in the classification of autism. It was compared with a more traditional approach, simultaneous and stepwise linear discriminant analyses, in terms of the ability of each methodology to both classify and predict persons as having autism or mental retardation based on information obtained from a new structured parent interview: the Autistic Behavior Interview. The neural network methodology was superior to discriminant function analysis both in its ability to classify groups (92 vs. 85%) and to generalize to new cases that were not part of the training sample (92 vs. 82%). Interrater and test-retest reliabilities and measures of internal consistency were satisfactory for most of the subscales in the Autistic Behavior Interview. The implications of neural network technology for diagnosis, in general, and for understanding of possible core deficits in autism are discussed. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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