66 recherche sur le mot-clé 'machine learning'

Machine Learning-Based Early Prediction Model for Autism Spectrum Disorder in Infants Using Acoustic Feature / Shengjian YIN in Autism Research, 19-3 (March 2026)  

Public ISBD [article]  inAutism Research > 19-3 (March 2026) . - e70179 Titre : Machine Learning-Based Early Prediction Model for Autism Spectrum Disorder in Infants Using Acoustic Feature Type de document : texte imprimé Auteurs : Shengjian YIN, Auteur ; Zhijia LI, Auteur ; Luyang GUAN, Auteur ; Zenghe YUE, Auteur ; Jincen WANG, Auteur ; Jinyi ZHU, Auteur ; Yazhu HAN, Auteur ; Qian LI, Auteur ; Lan LIN, Auteur ; Yaxin DAI, Auteur ; Haozhen CHEN, Auteur ; Yuheng CHEN, Auteur ; Yun LI, Auteur ; Xiaoyan KE, Auteur Article en page(s) : e70179 Langues : Anglais (eng) Mots-clés : acoustic features  autism spectrum disorder  machine learning  model  support vector machine Index. décimale : PER Périodiques Résumé : ABSTRACT This study aimed to create a machine learning-based predictive model for early detection of autism spectrum disorder (ASD) in infants using acoustic features. Conducted as a prospective cohort at Nanjing Medical University from 2019 to 2024, infants aged 9?18?months from an ASD sibling cohort participated. Behavioral and vocalization data were gathered during the Still-Face Paradigm, with ASD diagnoses confirmed at 36?months through ADOS and ADI-R assessments. Researchers extracted 4368 acoustic features from the recordings and applied LASSO regression for dimensionality reduction, identifying 39 key features. A support vector machine (SVM) classifier was then developed, tested with four kernel functions?linear, radial basis function, polynomial, and sigmoid?via tenfold cross-validation. The final sample included 88 infants, 28 of whom were diagnosed with ASD. The sigmoid kernel yielded the best results, achieving a 92.86% sensitivity, 93.33% specificity, and a 93.18% accuracy. Notably, spectral and energy-related features were significantly higher in ASD infants (p? En ligne : https://doi.org/10.1002/aur.70179 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=583 [article] Machine Learning-Based Early Prediction Model for Autism Spectrum Disorder in Infants Using Acoustic Feature [texte imprimé] / Shengjian YIN, Auteur ; Zhijia LI, Auteur ; Luyang GUAN, Auteur ; Zenghe YUE, Auteur ; Jincen WANG, Auteur ; Jinyi ZHU, Auteur ; Yazhu HAN, Auteur ; Qian LI, Auteur ; Lan LIN, Auteur ; Yaxin DAI, Auteur ; Haozhen CHEN, Auteur ; Yuheng CHEN, Auteur ; Yun LI, Auteur ; Xiaoyan KE, Auteur . - e70179. Langues : Anglais (eng) in Autism Research > 19-3 (March 2026) . - e70179 Mots-clés : acoustic features  autism spectrum disorder  machine learning  model  support vector machine Index. décimale : PER Périodiques Résumé : ABSTRACT This study aimed to create a machine learning-based predictive model for early detection of autism spectrum disorder (ASD) in infants using acoustic features. Conducted as a prospective cohort at Nanjing Medical University from 2019 to 2024, infants aged 9?18?months from an ASD sibling cohort participated. Behavioral and vocalization data were gathered during the Still-Face Paradigm, with ASD diagnoses confirmed at 36?months through ADOS and ADI-R assessments. Researchers extracted 4368 acoustic features from the recordings and applied LASSO regression for dimensionality reduction, identifying 39 key features. A support vector machine (SVM) classifier was then developed, tested with four kernel functions?linear, radial basis function, polynomial, and sigmoid?via tenfold cross-validation. The final sample included 88 infants, 28 of whom were diagnosed with ASD. The sigmoid kernel yielded the best results, achieving a 92.86% sensitivity, 93.33% specificity, and a 93.18% accuracy. Notably, spectral and energy-related features were significantly higher in ASD infants (p? En ligne : https://doi.org/10.1002/aur.70179 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=583

Machine learning prediction of conduct problems in children using the longitudinal ABCD study / Kathryn BERLUTI in Journal of Child Psychology and Psychiatry, 67-3 (March 2026)  

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 67-3 (March 2026) . - p.390-399 Titre : Machine learning prediction of conduct problems in children using the longitudinal ABCD study Type de document : texte imprimé Auteurs : Kathryn BERLUTI, Auteur ; Paige AMORMINO, Auteur ; Alexandra POTTER, Auteur ; Safwan WSHAH, Auteur ; Abigail MARSH, Auteur Article en page(s) : p.390-399 Langues : Anglais (eng) Mots-clés : Conduct disorder  conduct problems  machine learning  ABCD study Index. décimale : PER Périodiques Résumé : Background Children with conduct problems are at elevated risk for negative psychosocial, educational, and behavioral outcomes. Identifying at-risk children can aid in providing timely intervention and prevention, ultimately improving their long-term outcomes. There is a need to develop screening tools to better identify at-risk children who may benefit from early intervention. Methods Data were collected from the longitudinal Adolescent Brain Cognitive Development (ABCD) Study. Children completed a baseline visit at age 9?10, then returned annually for 3?years (n?=?3,517). We used machine learning classifiers (logistic regression, Naïve Bayes, support vector machine, and random forest) to predict conduct problems (i.e., conduct disorder or oppositional defiant disorder) in children after 1, 2, and 3?years. Results The best-performing model (the random forest classifier) predicted children at risk for conduct problems with an accuracy of 90% or greater (AUC?=?0.98 at 1?year, AUC?=?0.97 at 2?years, AUC?=?0.97 at 3?years). A random forest classifier simplified to include only 10 features was able to predict conduct problems nearly as well (AUC?=?0.97 at 1?year, AUC?=?0.96 at 2?years, AUC?=?0.97 at 3?years). Conclusions Using factors previously linked to conduct problems, we built machine learning models to identify predictors of conduct problems in children over a 3-year period. A small number of self-report features can be used to predict persistent conduct problems with 90% or greater specificity and sensitivity up to 3?years after initial assessment. This suggests that parent and child self-report data, along with machine learning, can identify children at risk for persistent conduct problems. En ligne : https://doi.org/10.1111/jcpp.70057 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=580 [article] Machine learning prediction of conduct problems in children using the longitudinal ABCD study [texte imprimé] / Kathryn BERLUTI, Auteur ; Paige AMORMINO, Auteur ; Alexandra POTTER, Auteur ; Safwan WSHAH, Auteur ; Abigail MARSH, Auteur . - p.390-399. Langues : Anglais (eng) in Journal of Child Psychology and Psychiatry > 67-3 (March 2026) . - p.390-399 Mots-clés : Conduct disorder  conduct problems  machine learning  ABCD study Index. décimale : PER Périodiques Résumé : Background Children with conduct problems are at elevated risk for negative psychosocial, educational, and behavioral outcomes. Identifying at-risk children can aid in providing timely intervention and prevention, ultimately improving their long-term outcomes. There is a need to develop screening tools to better identify at-risk children who may benefit from early intervention. Methods Data were collected from the longitudinal Adolescent Brain Cognitive Development (ABCD) Study. Children completed a baseline visit at age 9?10, then returned annually for 3?years (n?=?3,517). We used machine learning classifiers (logistic regression, Naïve Bayes, support vector machine, and random forest) to predict conduct problems (i.e., conduct disorder or oppositional defiant disorder) in children after 1, 2, and 3?years. Results The best-performing model (the random forest classifier) predicted children at risk for conduct problems with an accuracy of 90% or greater (AUC?=?0.98 at 1?year, AUC?=?0.97 at 2?years, AUC?=?0.97 at 3?years). A random forest classifier simplified to include only 10 features was able to predict conduct problems nearly as well (AUC?=?0.97 at 1?year, AUC?=?0.96 at 2?years, AUC?=?0.97 at 3?years). Conclusions Using factors previously linked to conduct problems, we built machine learning models to identify predictors of conduct problems in children over a 3-year period. A small number of self-report features can be used to predict persistent conduct problems with 90% or greater specificity and sensitivity up to 3?years after initial assessment. This suggests that parent and child self-report data, along with machine learning, can identify children at risk for persistent conduct problems. En ligne : https://doi.org/10.1111/jcpp.70057 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=580

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

Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field / Shyam Sundar RAJAGOPALAN in Journal of Neurodevelopmental Disorders, 16 (2024)  

Public ISBD [article]  inJournal of Neurodevelopmental Disorders > 16 (2024) Titre : Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field Type de document : texte imprimé Auteurs : Shyam Sundar RAJAGOPALAN, Auteur ; Kristiina TAMMIMIES, Auteur Langues : Anglais (eng) Mots-clés : Humans  Machine Learning  Electronic Health Records  Neurodevelopmental Disorders/diagnosis/epidemiology  Attention Deficit Disorder with Hyperactivity/diagnosis/epidemiology  Autism Spectrum Disorder/diagnosis/epidemiology  Electronic Health Record  Neurodevelopmental Disorder  Population Register  for publication Not applicable. Competing interests The authors declare that  there are no competing interests. Index. décimale : PER Périodiques Résumé : Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010-2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in 82% of studies and in particular tree-based prediction models performed well. The sensitivity of the models was lower than 75% for most studies, while the area under the curve (AUC) was greater than 75%. The most important predictors were patient and familial medical history and sociodemographic factors. Using private in-house datasets makes comparing and validating model generalizability across studies difficult. The ML model development and reporting guidelines were adopted only in a few recently reported studies. More work is needed to harness the power of data for detecting NDDs early. En ligne : https://dx.doi.org/10.1186/s11689-024-09579-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=576 [article] Predicting neurodevelopmental disorders using machine learning models and electronic health records - status of the field [texte imprimé] / Shyam Sundar RAJAGOPALAN, Auteur ; Kristiina TAMMIMIES, Auteur. Langues : Anglais (eng) in Journal of Neurodevelopmental Disorders > 16 (2024) Mots-clés : Humans  Machine Learning  Electronic Health Records  Neurodevelopmental Disorders/diagnosis/epidemiology  Attention Deficit Disorder with Hyperactivity/diagnosis/epidemiology  Autism Spectrum Disorder/diagnosis/epidemiology  Electronic Health Record  Neurodevelopmental Disorder  Population Register  for publication Not applicable. Competing interests The authors declare that  there are no competing interests. Index. décimale : PER Périodiques Résumé : Machine learning (ML) is increasingly used to identify patterns that could predict neurodevelopmental disorders (NDDs), such as autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). One key source of multilevel data for ML prediction models includes population-based registers and electronic health records. These can contain rich information on individual and familial medical histories and socio-demographics. This review summarizes studies published between 2010-2022 that used ML algorithms to develop predictive models for NDDs using population-based registers and electronic health records. A literature search identified 1191 articles, of which 32 were retained. Of these, 47% developed ASD prediction models and 25% ADHD models. Classical ML methods were used in 82% of studies and in particular tree-based prediction models performed well. The sensitivity of the models was lower than 75% for most studies, while the area under the curve (AUC) was greater than 75%. The most important predictors were patient and familial medical history and sociodemographic factors. Using private in-house datasets makes comparing and validating model generalizability across studies difficult. The ML model development and reporting guidelines were adopted only in a few recently reported studies. More work is needed to harness the power of data for detecting NDDs early. En ligne : https://dx.doi.org/10.1186/s11689-024-09579-0 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=576

Vocal markers of autism: Assessing the generalizability of machine learning models / Astrid RYBNER in Autism Research, 15-6 (June 2022)  

Public ISBD [article]  inAutism Research > 15-6 (June 2022) . - p.1018-1030 Titre : Vocal markers of autism: Assessing the generalizability of machine learning models Type de document : texte imprimé Auteurs : Astrid RYBNER, Auteur ; Emil TRENCKNER JESSEN, Auteur ; Marie DAMSGAARD MORTENSEN, Auteur ; Stine Nyhus LARSEN, Auteur ; Ruth B. 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 [article] Vocal markers of autism: Assessing the generalizability of machine learning models [texte imprimé] / Astrid RYBNER, Auteur ; Emil TRENCKNER JESSEN, Auteur ; Marie DAMSGAARD MORTENSEN, Auteur ; Stine Nyhus LARSEN, Auteur ; Ruth B. 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 / Mariano ALCAÑIZ in Autism Research, 15-1 (January 2022)  

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Moderators of response to child-based and parent-based child anxiety treatment: a machine learning-based analysis / Eli R. LEBOWITZ in Journal of Child Psychology and Psychiatry, 62-10 (October 2021)  

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Objective approach to diagnosing attention deficit hyperactivity disorder by using pixel subtraction and machine learning classification of outpatient consultation videos / Yi-Hung CHIU in Journal of Neurodevelopmental Disorders, 16 (2024)  

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Annual Research Review: Translational machine learning for child and adolescent psychiatry / Dominic DWYER in Journal of Child Psychology and Psychiatry, 63-4 (April 2022)  

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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)  

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