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 Auteurs : Daniel BONE, Auteur ; Matthew 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] / Daniel BONE, Auteur ; Matthew 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 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] / 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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Motion-Capture Patterns of Voluntarily Mimicked Dynamic Facial Expressions in Children and Adolescents With and Without ASD / Emily ZANE in Journal of Autism and Developmental Disorders, 49-3 (March 2019)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 49-3 (March 2019) . - p.1062-1079 Titre : Motion-Capture Patterns of Voluntarily Mimicked Dynamic Facial Expressions in Children and Adolescents With and Without ASD Type de document : texte imprimé Auteurs : Emily ZANE, Auteur ; Zhaojun YANG, Auteur ; Lucia POZZAN, Auteur ; Tanaya GUHA, Auteur ; Shrikanth NARAYANAN, Auteur ; Ruth B. GROSSMAN, Auteur Article en page(s) : p.1062-1079 Langues : Anglais (eng) Mots-clés : Asd  Emotional facial expressions  Motion capture  Social communication Index. décimale : PER Périodiques Résumé : Research shows that neurotypical individuals struggle to interpret the emotional facial expressions of people with Autism Spectrum Disorder (ASD). The current study uses motion-capture to objectively quantify differences between the movement patterns of emotional facial expressions of individuals with and without ASD. Participants volitionally mimicked emotional expressions while wearing facial markers. Recorded marker movement was grouped by expression valence and intensity. We used Growth Curve Analysis to test whether movement patterns were predictable by expression type and participant group. Results show significant interactions between expression type and group, and little effect of emotion valence on ASD expressions. Together, results support perceptions that expressions of individuals with ASD are different from-and more ambiguous than-those of neurotypical individuals'. En ligne : http://dx.doi.org/10.1007/s10803-018-3811-7 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=386 [article] Motion-Capture Patterns of Voluntarily Mimicked Dynamic Facial Expressions in Children and Adolescents With and Without ASD [texte imprimé] / Emily ZANE, Auteur ; Zhaojun YANG, Auteur ; Lucia POZZAN, Auteur ; Tanaya GUHA, Auteur ; Shrikanth NARAYANAN, Auteur ; Ruth B. GROSSMAN, Auteur . - p.1062-1079. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 49-3 (March 2019) . - p.1062-1079 Mots-clés : Asd  Emotional facial expressions  Motion capture  Social communication Index. décimale : PER Périodiques Résumé : Research shows that neurotypical individuals struggle to interpret the emotional facial expressions of people with Autism Spectrum Disorder (ASD). The current study uses motion-capture to objectively quantify differences between the movement patterns of emotional facial expressions of individuals with and without ASD. Participants volitionally mimicked emotional expressions while wearing facial markers. Recorded marker movement was grouped by expression valence and intensity. We used Growth Curve Analysis to test whether movement patterns were predictable by expression type and participant group. Results show significant interactions between expression type and group, and little effect of emotion valence on ASD expressions. Together, results support perceptions that expressions of individuals with ASD are different from-and more ambiguous than-those of neurotypical individuals'. En ligne : http://dx.doi.org/10.1007/s10803-018-3811-7 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=386

Strategies for Disseminating Information on Biomedical Research on Autism to Hispanic Parents / Clara M. LAJONCHERE in Journal of Autism and Developmental Disorders, 46-3 (March 2016)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 46-3 (March 2016) . - p.1038-1050 Titre : Strategies for Disseminating Information on Biomedical Research on Autism to Hispanic Parents Type de document : texte imprimé Auteurs : Clara M. LAJONCHERE, Auteur ; Barbara Y. WHEELER, Auteur ; Thomas W. VALENTE, Auteur ; Cary KREUTZER, Auteur ; Aron MUNSON, Auteur ; Shrikanth NARAYANAN, Auteur ; Abe KAZEMZADEH, Auteur ; Roxana CRUZ, Auteur ; Irene MARTINEZ, Auteur ; Sheree M. SCHRAGER, Auteur ; Lisa SCHWEITZER, Auteur ; Tara CHKLOVSKI, Auteur ; Darryl HWANG, Auteur Article en page(s) : p.1038-1050 Langues : Anglais (eng) Mots-clés : Biomedical research on Autism  Hispanic/Latino  Health literacy  Racial disparities Index. décimale : PER Périodiques Résumé : Low income Hispanic families experience multiple barriers to accessing evidence-based information on Autism Spectrum Disorders (ASD). This study utilized a mixed-strategy intervention to create access to information in published bio-medical research articles on ASD by distilling the content into parent-friendly English- and Spanish-language ASD Science Briefs and presenting them to participants using two socially-oriented dissemination methods. There was a main effect for short-term knowledge gains associated with the Science Briefs but no effect for the dissemination method. After 5 months, participants reported utilizing the information learned and 90 % wanted to read more Science Briefs. These preliminary findings highlight the potential benefits of distilling biomedical research articles on ASD into parent-friendly educational products for currently underserved Hispanic parents. En ligne : http://dx.doi.org/10.1007/s10803-015-2649-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=281 [article] Strategies for Disseminating Information on Biomedical Research on Autism to Hispanic Parents [texte imprimé] / Clara M. LAJONCHERE, Auteur ; Barbara Y. WHEELER, Auteur ; Thomas W. VALENTE, Auteur ; Cary KREUTZER, Auteur ; Aron MUNSON, Auteur ; Shrikanth NARAYANAN, Auteur ; Abe KAZEMZADEH, Auteur ; Roxana CRUZ, Auteur ; Irene MARTINEZ, Auteur ; Sheree M. SCHRAGER, Auteur ; Lisa SCHWEITZER, Auteur ; Tara CHKLOVSKI, Auteur ; Darryl HWANG, Auteur . - p.1038-1050. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 46-3 (March 2016) . - p.1038-1050 Mots-clés : Biomedical research on Autism  Hispanic/Latino  Health literacy  Racial disparities Index. décimale : PER Périodiques Résumé : Low income Hispanic families experience multiple barriers to accessing evidence-based information on Autism Spectrum Disorders (ASD). This study utilized a mixed-strategy intervention to create access to information in published bio-medical research articles on ASD by distilling the content into parent-friendly English- and Spanish-language ASD Science Briefs and presenting them to participants using two socially-oriented dissemination methods. There was a main effect for short-term knowledge gains associated with the Science Briefs but no effect for the dissemination method. After 5 months, participants reported utilizing the information learned and 90 % wanted to read more Science Briefs. These preliminary findings highlight the potential benefits of distilling biomedical research articles on ASD into parent-friendly educational products for currently underserved Hispanic parents. En ligne : http://dx.doi.org/10.1007/s10803-015-2649-5 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=281

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 Auteurs : Daniel BONE, Auteur ; Somer L. BISHOP, Auteur ; Matthew P. BLACK, Auteur ; Matthew GOODWIN, Auteur ; Catherine LORD, Auteur ; Shrikanth 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] / Daniel BONE, Auteur ; Somer L. BISHOP, Auteur ; Matthew P. BLACK, Auteur ; Matthew GOODWIN, Auteur ; Catherine LORD, Auteur ; Shrikanth 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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