[article] 
inJournal of Child Psychology and Psychiatry > 57-8 (August 2016) . - p.927-937

Titre :	Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion
Type de document :	Texte imprimé et/ou numérique
Auteurs :	Daniel BONE, Auteur ; Somer L. BISHOP, Auteur ; Matthew P. BLACK, Auteur ; Matthew S. GOODWIN, Auteur ; Catherine LORD, Auteur ; Shrikanth S. NARAYANAN, Auteur
Article en page(s) :	p.927-937
Langues :	Anglais (eng)
Mots-clés :	Autism  screening  diagnosis  machine learning
Index. décimale :	PER Périodiques
Résumé :	Background Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools. Methods The data consisted of Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non-ASD developmental or psychiatric disorders, split at age 10. Algorithms were created via a robust ML classifier, support vector machine, while targeting best-estimate clinical diagnosis of ASD versus non-ASD. Parameter settings were tuned in multiple levels of cross-validation. Results The created algorithms were more effective (higher performing) than the current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near-peak performance with five or fewer codes). Results from ML-based fusion of ADI-R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes. Conclusions
En ligne :	http://dx.doi.org/10.1111/jcpp.12559
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=292

[article] Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion [Texte imprimé et/ou numérique] / Daniel BONE, Auteur ; Somer L. BISHOP, Auteur ; Matthew P. BLACK, Auteur ; Matthew S. GOODWIN, Auteur ; Catherine LORD, Auteur ; Shrikanth S. NARAYANAN, Auteur . - p.927-937.
Langues : Anglais (eng)
in Journal of Child Psychology and Psychiatry > 57-8 (August 2016) . - p.927-937

Mots-clés :	Autism  screening  diagnosis  machine learning
Index. décimale :	PER Périodiques
Résumé :	Background Machine learning (ML) provides novel opportunities for human behavior research and clinical translation, yet its application can have noted pitfalls (Bone et al., 2015). In this work, we fastidiously utilize ML to derive autism spectrum disorder (ASD) instrument algorithms in an attempt to improve upon widely used ASD screening and diagnostic tools. Methods The data consisted of Autism Diagnostic Interview-Revised (ADI-R) and Social Responsiveness Scale (SRS) scores for 1,264 verbal individuals with ASD and 462 verbal individuals with non-ASD developmental or psychiatric disorders, split at age 10. Algorithms were created via a robust ML classifier, support vector machine, while targeting best-estimate clinical diagnosis of ASD versus non-ASD. Parameter settings were tuned in multiple levels of cross-validation. Results The created algorithms were more effective (higher performing) than the current algorithms, were tunable (sensitivity and specificity can be differentially weighted), and were more efficient (achieving near-peak performance with five or fewer codes). Results from ML-based fusion of ADI-R and SRS are reported. We present a screener algorithm for below (above) age 10 that reached 89.2% (86.7%) sensitivity and 59.0% (53.4%) specificity with only five behavioral codes. Conclusions
En ligne :	http://dx.doi.org/10.1111/jcpp.12559
Permalink :	https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=292