Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises.
Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of...
| Publicado en: | Journal of Autism & Developmental Disorders Vol. 45; no. 5; pp. 1121 - 1137 |
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| Autores principales: | , , , , , |
| Formato: | algorithm research tables/charts Journal Article |
| Publicado: |
Springer Nature
May2015
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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=103791748&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 103791748 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01623257 AUT jtl: Journal of Autism & Developmental Disorders issn: 01623257 maglogo: N pubinfo: dt: May2015 vid: 45 iid: 5 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 103791748 102202098 10.1007/s10803-014-2268-6 NLM25294649 103791748 ppf: 1121 ppct: 16 formats: fmt: @attributes: type: P tig: atl: Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises. aug: au: Bone, Daniel Goodwin, Matthew Black, Matthew Lee, Chi-Chun Audhkhasi, Kartik Narayanan, Shrikanth affil: Signal Analysis & Interpretation Laboratory (SAIL), University of Southern California, 3710 McClintock Ave. Los Angeles 90089 USA sug: subj: Autism Spectrum Disorder Diagnosis Diagnosis, Computer Assisted In Infancy and Childhood Algorithms Utilization Human Signal Processing, Computer Assisted Checklists Interview Guides Conceptual Framework Experimental Studies Child, Preschool Child Replication Studies Diagnostic Errors Child, Preschool: 2-5 years Child: 6-12 years ab: Machine learning has immense potential to enhance diagnostic and intervention research in the behavioral sciences, and may be especially useful in investigations involving the highly prevalent and heterogeneous syndrome of autism spectrum disorder. However, use of machine learning in the absence of clinical domain expertise can be tenuous and lead to misinformed conclusions. To illustrate this concern, the current paper critically evaluates and attempts to reproduce results from two studies (Wall et al. in Transl Psychiatry 2(4):e100, ; PloS One 7(8), ) that claim to drastically reduce time to diagnose autism using machine learning. Our failure to generate comparable findings to those reported by Wall and colleagues using larger and more balanced data underscores several conceptual and methodological problems associated with these studies. We conclude with proposed best-practices when using machine learning in autism research, and highlight some especially promising areas for collaborative work at the intersection of computational and behavioral science. pubtype: Academic Journal doctype: algorithm research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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