Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises / Daniel BONE in Journal of Autism and Developmental Disorders, 45-5 (May 2015)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 45-5 (May 2015) . - p.1121-1136 Titre : Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises Type de document : Texte imprimé et/ou numérique Auteurs : Daniel BONE, Auteur ; Matthew S. GOODWIN, Auteur ; Matthew P. BLACK, Auteur ; Chi-Chun LEE, Auteur ; Kartik AUDHKHASI, Auteur ; Shrikanth NARAYANAN, Auteur Article en page(s) : p.1121-1136 Langues : Anglais (eng) Mots-clés : Autism diagnostic observation schedule  Autism diagnostic interview  Machine learning  Signal processing  Autism  Diagnosis Index. décimale : PER Périodiques Résumé : 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, 2012a; PloS One 7(8), 2012b) 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. En ligne : http://dx.doi.org/10.1007/s10803-014-2268-6 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=259 [article] Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises [Texte imprimé et/ou numérique] / Daniel BONE, Auteur ; Matthew S. GOODWIN, Auteur ; Matthew P. BLACK, Auteur ; Chi-Chun LEE, Auteur ; Kartik AUDHKHASI, Auteur ; Shrikanth NARAYANAN, Auteur . - p.1121-1136. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 45-5 (May 2015) . - p.1121-1136 Mots-clés : Autism diagnostic observation schedule  Autism diagnostic interview  Machine learning  Signal processing  Autism  Diagnosis Index. décimale : PER Périodiques Résumé : 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, 2012a; PloS One 7(8), 2012b) 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. En ligne : http://dx.doi.org/10.1007/s10803-014-2268-6 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=259

Behavioral signal processing and autism. Learning from multimodal behavioral signals / Daniel BONE

Public ISBD in Autism imaging and Devices / Manuel F. CASANOVA Titre : Behavioral signal processing and autism. Learning from multimodal behavioral signals Type de document : Texte imprimé et/ou numérique Auteurs : Daniel BONE, Auteur ; Theodora CHASPARI, Auteur ; Shrikanth NARAYANAN, Auteur Importance : p.319-344 Langues : Anglais (eng) Index. décimale : SCI-D SCI-D - Neurosciences Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=382 in Autism imaging and Devices / Manuel F. CASANOVA Behavioral signal processing and autism. Learning from multimodal behavioral signals [Texte imprimé et/ou numérique] / Daniel BONE, Auteur ; Theodora CHASPARI, Auteur ; Shrikanth NARAYANAN, Auteur . - [s.d.] . - p.319-344. Langues : Anglais (eng) Index. décimale : SCI-D SCI-D - Neurosciences Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=382

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Use of machine learning to improve autism screening and diagnostic instruments: effectiveness, efficiency, and multi-instrument fusion / Daniel BONE in Journal of Child Psychology and Psychiatry, 57-8 (August 2016)  

Public ISBD [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

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