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Applying Machine Learning to Facilitate Autism Diagnostics: Pitfalls and Promises / Daniel BONE in Journal of Autism and Developmental Disorders, 45-5 (May 2015)
[article]
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
in Journal of Autism and Developmental Disorders > 45-5 (May 2015) . - p.1121-1136[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 Vocal markers of autism: Assessing the generalizability of machine learning models / Astrid RYBNER in Autism Research, 15-6 (June 2022)
[article]
Titre : Vocal markers of autism: Assessing the generalizability of machine learning models Type de document : Texte imprimé et/ou numérique Auteurs : Astrid RYBNER, Auteur ; Emil TRENCKNER JESSEN, Auteur ; Marie DAMSGAARD MORTENSEN, Auteur ; Stine Nyhus LARSEN, Auteur ; Ruth GROSSMAN, Auteur ; Niels BILENBERG, Auteur ; Cathriona CANTIO, Auteur ; Jens Richardt MØLLEGAARD JEPSEN, Auteur ; Ethan WEED, Auteur ; Arndis SIMONSEN, Auteur ; Riccardo FUSAROLI, Auteur Article en page(s) : p.1018-1030 Langues : Anglais (eng) Mots-clés : Autism Spectrum Disorder Autistic Disorder/diagnosis Biomarkers Humans Machine Learning Speech Voice biobehavioral markers generalizability Index. décimale : PER Périodiques Résumé : Machine learning (ML) approaches show increasing promise in their ability to identify vocal markers of autism. Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected, for example, using a different speech task or in a different language. In this paper, we systematically assess the generalizability of ML findings across a variety of contexts. We train promising published ML models of vocal markers of autism on novel cross-linguistic datasets following a rigorous pipeline to minimize overfitting, including cross-validated training and ensemble models. We test the generalizability of the models by testing them on (i) different participants from the same study, performing the same task; (ii) the same participants, performing a different (but similar) task; (iii) a different study with participants speaking a different language, performing the same type of task. While model performance is similar to previously published findings when trained and tested on data from the same study (out-of-sample performance), there is considerable variance between studies. Crucially, the models do not generalize well to different, though similar, tasks and not at all to new languages. The ML pipeline is openly shared. Generalizability of ML models of vocal markers of autism is an issue. We outline three recommendations for strategies researchers could take to be more explicit about generalizability and improve it in future studies. LAY SUMMARY: Machine learning approaches promise to be able to identify autism from voice only. These models underestimate how diverse the contexts in which we speak are, how diverse the languages used are and how diverse autistic voices are. Machine learning approaches need to be more careful in defining their limits and generalizability. En ligne : http://dx.doi.org/10.1002/aur.2721 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=476
in Autism Research > 15-6 (June 2022) . - p.1018-1030[article] Vocal markers of autism: Assessing the generalizability of machine learning models [Texte imprimé et/ou numérique] / Astrid RYBNER, Auteur ; Emil TRENCKNER JESSEN, Auteur ; Marie DAMSGAARD MORTENSEN, Auteur ; Stine Nyhus LARSEN, Auteur ; Ruth GROSSMAN, Auteur ; Niels BILENBERG, Auteur ; Cathriona CANTIO, Auteur ; Jens Richardt MØLLEGAARD JEPSEN, Auteur ; Ethan WEED, Auteur ; Arndis SIMONSEN, Auteur ; Riccardo FUSAROLI, Auteur . - p.1018-1030.
Langues : Anglais (eng)
in Autism Research > 15-6 (June 2022) . - p.1018-1030
Mots-clés : Autism Spectrum Disorder Autistic Disorder/diagnosis Biomarkers Humans Machine Learning Speech Voice biobehavioral markers generalizability Index. décimale : PER Périodiques Résumé : Machine learning (ML) approaches show increasing promise in their ability to identify vocal markers of autism. Nonetheless, it is unclear to what extent such markers generalize to new speech samples collected, for example, using a different speech task or in a different language. In this paper, we systematically assess the generalizability of ML findings across a variety of contexts. We train promising published ML models of vocal markers of autism on novel cross-linguistic datasets following a rigorous pipeline to minimize overfitting, including cross-validated training and ensemble models. We test the generalizability of the models by testing them on (i) different participants from the same study, performing the same task; (ii) the same participants, performing a different (but similar) task; (iii) a different study with participants speaking a different language, performing the same type of task. While model performance is similar to previously published findings when trained and tested on data from the same study (out-of-sample performance), there is considerable variance between studies. Crucially, the models do not generalize well to different, though similar, tasks and not at all to new languages. The ML pipeline is openly shared. Generalizability of ML models of vocal markers of autism is an issue. We outline three recommendations for strategies researchers could take to be more explicit about generalizability and improve it in future studies. LAY SUMMARY: Machine learning approaches promise to be able to identify autism from voice only. These models underestimate how diverse the contexts in which we speak are, how diverse the languages used are and how diverse autistic voices are. Machine learning approaches need to be more careful in defining their limits and generalizability. En ligne : http://dx.doi.org/10.1002/aur.2721 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=476 Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis / M. ALCAÑIZ in Autism Research, 15-1 (January 2022)
[article]
Titre : Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis Type de document : Texte imprimé et/ou numérique Auteurs : M. ALCAÑIZ, Auteur ; I. A. CHICCHI-GIGLIOLI, Auteur ; L. A. CARRASCO-RIBELLES, Auteur ; J. MARÍN-MORALES, Auteur ; M. E. MINISSI, Auteur ; G. TERUEL-GARCÍA, Auteur ; M. SIRERA, Auteur ; L. ABAD, Auteur Article en page(s) : p.131-145 Langues : Anglais (eng) Mots-clés : Adult Autism Spectrum Disorder/diagnosis Biomarkers Child Fixation, Ocular Humans Machine Learning Virtual Reality autism spectrum disorder behavioral biomarker eye tracking machine learning multivariate supervised learning Index. décimale : PER Périodiques Résumé : The core symptoms of autism spectrum disorder (ASD) mainly relate to social communication and interactions. ASD assessment involves expert observations in neutral settings, which introduces limitations and biases related to lack of objectivity and does not capture performance in real-world settings. To overcome these limitations, advances in technologies (e.g., virtual reality) and sensors (e.g., eye-tracking tools) have been used to create realistic simulated environments and track eye movements, enriching assessments with more objective data than can be obtained via traditional measures. This study aimed to distinguish between autistic and typically developing children using visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to and extraction of socially relevant information. The 55 children participated. Autistic children presented a higher number of frames, both overall and per scenario, and showed higher visual preferences for adults over children, as well as specific preferences for adults' rather than children's faces on which looked more at bodies. A set of multivariate supervised machine learning models were developed using recursive feature selection to recognize ASD based on extracted eye gaze features. The models achieved up to 86% accuracy (sensitivity = 91%) in recognizing autistic children. Our results should be taken as preliminary due to the relatively small sample size and the lack of an external replication dataset. However, to our knowledge, this constitutes a first proof of concept in the combined use of virtual reality, eye-tracking tools, and machine learning for ASD recognition. LAY SUMMARY: Core symptoms in children with ASD involve social communication and interaction. ASD assessment includes expert observations in neutral settings, which show limitations and biases related to lack of objectivity and do not capture performance in real settings. To overcome these limitations, this work aimed to distinguish between autistic and typically developing children in visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to, and extraction of, socially relevant information. En ligne : http://dx.doi.org/10.1002/aur.2636 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=450
in Autism Research > 15-1 (January 2022) . - p.131-145[article] Eye gaze as a biomarker in the recognition of autism spectrum disorder using virtual reality and machine learning: A proof of concept for diagnosis [Texte imprimé et/ou numérique] / M. ALCAÑIZ, Auteur ; I. A. CHICCHI-GIGLIOLI, Auteur ; L. A. CARRASCO-RIBELLES, Auteur ; J. MARÍN-MORALES, Auteur ; M. E. MINISSI, Auteur ; G. TERUEL-GARCÍA, Auteur ; M. SIRERA, Auteur ; L. ABAD, Auteur . - p.131-145.
Langues : Anglais (eng)
in Autism Research > 15-1 (January 2022) . - p.131-145
Mots-clés : Adult Autism Spectrum Disorder/diagnosis Biomarkers Child Fixation, Ocular Humans Machine Learning Virtual Reality autism spectrum disorder behavioral biomarker eye tracking machine learning multivariate supervised learning Index. décimale : PER Périodiques Résumé : The core symptoms of autism spectrum disorder (ASD) mainly relate to social communication and interactions. ASD assessment involves expert observations in neutral settings, which introduces limitations and biases related to lack of objectivity and does not capture performance in real-world settings. To overcome these limitations, advances in technologies (e.g., virtual reality) and sensors (e.g., eye-tracking tools) have been used to create realistic simulated environments and track eye movements, enriching assessments with more objective data than can be obtained via traditional measures. This study aimed to distinguish between autistic and typically developing children using visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to and extraction of socially relevant information. The 55 children participated. Autistic children presented a higher number of frames, both overall and per scenario, and showed higher visual preferences for adults over children, as well as specific preferences for adults' rather than children's faces on which looked more at bodies. A set of multivariate supervised machine learning models were developed using recursive feature selection to recognize ASD based on extracted eye gaze features. The models achieved up to 86% accuracy (sensitivity = 91%) in recognizing autistic children. Our results should be taken as preliminary due to the relatively small sample size and the lack of an external replication dataset. However, to our knowledge, this constitutes a first proof of concept in the combined use of virtual reality, eye-tracking tools, and machine learning for ASD recognition. LAY SUMMARY: Core symptoms in children with ASD involve social communication and interaction. ASD assessment includes expert observations in neutral settings, which show limitations and biases related to lack of objectivity and do not capture performance in real settings. To overcome these limitations, this work aimed to distinguish between autistic and typically developing children in visual attention behaviors through an eye-tracking paradigm in a virtual environment as a measure of attunement to, and extraction of, socially relevant information. En ligne : http://dx.doi.org/10.1002/aur.2636 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=450 Moderators of response to child-based and parent-based child anxiety treatment: a machine learning-based analysis / E. R. LEBOWITZ in Journal of Child Psychology and Psychiatry, 62-10 (October 2021)
[article]
Titre : Moderators of response to child-based and parent-based child anxiety treatment: a machine learning-based analysis Type de document : Texte imprimé et/ou numérique Auteurs : E. R. LEBOWITZ, Auteur ; S. ZILCHA-MANO, Auteur ; M. ORBACH, Auteur ; Y. SHIMSHONI, Auteur ; W. K. SILVERMAN, Auteur Article en page(s) : p.1175-1182 Langues : Anglais (eng) Mots-clés : Anxiety Anxiety Disorders/therapy Child Cognitive Behavioral Therapy Humans Machine Learning Parenting Treatment Outcome behavior therapy machine learning parent training Index. décimale : PER Périodiques Résumé : BACKGROUND: Identifying moderators of response to treatment for childhood anxiety can inform clinical decision-making and improve overall treatment efficacy. We examined moderators of response to child-based cognitive-behavioral therapy (CBT) and parent-based SPACE (Supportive Parenting for Anxious Childhood Emotions) in a recent randomized clinical trial. METHODS: We applied a machine learning approach to identify moderators of treatment response to CBT versus SPACE, in a clinical trial of 124 children with primary anxiety disorders. We tested the clinical benefit of prescribing treatment based on the identified moderators by comparing outcomes for children randomly assigned to their optimal and nonoptimal treatment conditions. We further applied machine learning to explore relations between moderators and shed light on how they interact to predict outcomes. Potential moderators included demographic, socioemotional, parenting, and biological variables. We examined moderation separately for child-reported, parent-reported, and independent-evaluator-reported outcomes. RESULTS: Parent-reported outcomes were moderated by parent negativity and child oxytocin levels. Child-reported outcomes were moderated by baseline anxiety, parent negativity, and parent oxytocin levels. Independent-evaluator-reported outcomes were moderated by baseline anxiety. Children assigned to their optimal treatment condition had significantly greater reduction in anxiety symptoms, compared with children assigned to their nonoptimal treatment. Significant interactions emerged between the identified moderators. CONCLUSIONS: Our findings represent an important step toward optimizing treatment selection and increasing treatment efficacy. En ligne : http://dx.doi.org/10.1111/jcpp.13386 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=456
in Journal of Child Psychology and Psychiatry > 62-10 (October 2021) . - p.1175-1182[article] Moderators of response to child-based and parent-based child anxiety treatment: a machine learning-based analysis [Texte imprimé et/ou numérique] / E. R. LEBOWITZ, Auteur ; S. ZILCHA-MANO, Auteur ; M. ORBACH, Auteur ; Y. SHIMSHONI, Auteur ; W. K. SILVERMAN, Auteur . - p.1175-1182.
Langues : Anglais (eng)
in Journal of Child Psychology and Psychiatry > 62-10 (October 2021) . - p.1175-1182
Mots-clés : Anxiety Anxiety Disorders/therapy Child Cognitive Behavioral Therapy Humans Machine Learning Parenting Treatment Outcome behavior therapy machine learning parent training Index. décimale : PER Périodiques Résumé : BACKGROUND: Identifying moderators of response to treatment for childhood anxiety can inform clinical decision-making and improve overall treatment efficacy. We examined moderators of response to child-based cognitive-behavioral therapy (CBT) and parent-based SPACE (Supportive Parenting for Anxious Childhood Emotions) in a recent randomized clinical trial. METHODS: We applied a machine learning approach to identify moderators of treatment response to CBT versus SPACE, in a clinical trial of 124 children with primary anxiety disorders. We tested the clinical benefit of prescribing treatment based on the identified moderators by comparing outcomes for children randomly assigned to their optimal and nonoptimal treatment conditions. We further applied machine learning to explore relations between moderators and shed light on how they interact to predict outcomes. Potential moderators included demographic, socioemotional, parenting, and biological variables. We examined moderation separately for child-reported, parent-reported, and independent-evaluator-reported outcomes. RESULTS: Parent-reported outcomes were moderated by parent negativity and child oxytocin levels. Child-reported outcomes were moderated by baseline anxiety, parent negativity, and parent oxytocin levels. Independent-evaluator-reported outcomes were moderated by baseline anxiety. Children assigned to their optimal treatment condition had significantly greater reduction in anxiety symptoms, compared with children assigned to their nonoptimal treatment. Significant interactions emerged between the identified moderators. CONCLUSIONS: Our findings represent an important step toward optimizing treatment selection and increasing treatment efficacy. En ligne : http://dx.doi.org/10.1111/jcpp.13386 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=456 Annual Research Review: Translational machine learning for child and adolescent psychiatry / Dominic DWYER in Journal of Child Psychology and Psychiatry, 63-4 (April 2022)
[article]
Titre : Annual Research Review: Translational machine learning for child and adolescent psychiatry Type de document : Texte imprimé et/ou numérique Auteurs : Dominic DWYER, Auteur ; Nikolaos KOUTSOULERIS, Auteur Article en page(s) : p.421-443 Langues : Anglais (eng) Mots-clés : Adhd Machine learning artificial intelligence autism spectrum disorders depression psychosis Index. décimale : PER Périodiques Résumé : Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents. En ligne : http://dx.doi.org/10.1111/jcpp.13545 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=475
in Journal of Child Psychology and Psychiatry > 63-4 (April 2022) . - p.421-443[article] Annual Research Review: Translational machine learning for child and adolescent psychiatry [Texte imprimé et/ou numérique] / Dominic DWYER, Auteur ; Nikolaos KOUTSOULERIS, Auteur . - p.421-443.
Langues : Anglais (eng)
in Journal of Child Psychology and Psychiatry > 63-4 (April 2022) . - p.421-443
Mots-clés : Adhd Machine learning artificial intelligence autism spectrum disorders depression psychosis Index. décimale : PER Périodiques Résumé : Children and adolescents could benefit from the use of predictive tools that facilitate personalized diagnoses, prognoses, and treatment selection. Such tools have not yet been deployed using traditional statistical methods, potentially due to the limitations of the paradigm and the need to leverage large amounts of digital data. This review will suggest that a machine learning approach could address these challenges and is designed to introduce new readers to the background, methods, and results in the field. A rationale is first introduced followed by an outline of fundamental elements of machine learning approaches. To provide an overview of the use of the techniques in child and adolescent literature, a scoping review of broad trends is then presented. Selected studies are also highlighted in order to draw attention to research areas that are closest to translation and studies that exhibit a high degree of experimental innovation. Limitations to the research, and machine learning approaches generally, are outlined in the penultimate section highlighting issues related to sample sizes, validation, clinical utility, and ethical challenges. Finally, future directions are discussed that could enhance the possibility of clinical implementation and address specific questions relevant to the child and adolescent psychiatry. The review gives a broad overview of the machine learning paradigm in order to highlight the benefits of a shift in perspective towards practically oriented statistical solutions that aim to improve clinical care of children and adolescents. En ligne : http://dx.doi.org/10.1111/jcpp.13545 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=475 Assessment of the Autism Spectrum Disorder Based on Machine Learning and Social Visual Attention: A Systematic Review / Maria Eleonora MINISSI in Journal of Autism and Developmental Disorders, 52-5 (May 2022)
PermalinkAuditory repetition suppression alterations in relation to cognitive functioning in fragile X syndrome: a combined EEG and machine learning approach / I. S. KNOTH in Journal of Neurodevelopmental Disorders, 10-1 (December 2018)
PermalinkDetecting and Classifying Self-injurious Behavior in Autism Spectrum Disorder Using Machine Learning Techniques / Kristine D. CANTIN-GARSIDE in Journal of Autism and Developmental Disorders, 50-11 (November 2020)
PermalinkEvidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants / Arjun P. ATHREYA in Journal of Child Psychology and Psychiatry, 63-11 (November 2022)
PermalinkEye Tracking Biomarkers for Autism Spectrum Disorder Detection using Machine Learning and Deep Learning Techniques: Review / R. ASMETHA JEYARANI in Research in Autism Spectrum Disorders, 108 (October 2023)
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