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Evidence 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)
[article]
Titre : Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants Type de document : Texte imprimé et/ou numérique Auteurs : Arjun P. ATHREYA, Auteur ; Jennifer L. VANDE VOORT, Auteur ; Julia SHEKUNOV, Auteur ; Sandra J. RACKLEY, Auteur ; Jarrod M. LEFFLER, Auteur ; Alastair J. MCKEAN, Auteur ; Magdalena ROMANOWICZ, Auteur ; Betsy D. KENNARD, Auteur ; Graham J. EMSLIE, Auteur ; Taryn MAYES, Auteur ; Madhukar TRIVEDI, Auteur ; Liewei WANG, Auteur ; Richard M. WEINSHILBOUM, Auteur ; William V. BOBO, Auteur ; Paul E. CROARKIN, Auteur Article en page(s) : p.1347-1358 Langues : Anglais (eng) Mots-clés : Child Humans Adolescent Fluoxetine/therapeutic use Depressive Disorder, Major/therapy Duloxetine Hydrochloride/therapeutic use Artificial Intelligence Double-Blind Method Antidepressive Agents Treatment Outcome Machine Learning Depression adolescents decision support tools Index. décimale : PER Périodiques Résumé : BACKGROUND: The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. METHODS: The study samples included training datasets (N=271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N=255) or placebo (N=265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. RESULTS: Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. CONCLUSIONS: PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression. En ligne : http://dx.doi.org/10.1111/jcpp.13580 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490
in Journal of Child Psychology and Psychiatry > 63-11 (November 2022) . - p.1347-1358[article] Evidence for machine learning guided early prediction of acute outcomes in the treatment of depressed children and adolescents with antidepressants [Texte imprimé et/ou numérique] / Arjun P. ATHREYA, Auteur ; Jennifer L. VANDE VOORT, Auteur ; Julia SHEKUNOV, Auteur ; Sandra J. RACKLEY, Auteur ; Jarrod M. LEFFLER, Auteur ; Alastair J. MCKEAN, Auteur ; Magdalena ROMANOWICZ, Auteur ; Betsy D. KENNARD, Auteur ; Graham J. EMSLIE, Auteur ; Taryn MAYES, Auteur ; Madhukar TRIVEDI, Auteur ; Liewei WANG, Auteur ; Richard M. WEINSHILBOUM, Auteur ; William V. BOBO, Auteur ; Paul E. CROARKIN, Auteur . - p.1347-1358.
Langues : Anglais (eng)
in Journal of Child Psychology and Psychiatry > 63-11 (November 2022) . - p.1347-1358
Mots-clés : Child Humans Adolescent Fluoxetine/therapeutic use Depressive Disorder, Major/therapy Duloxetine Hydrochloride/therapeutic use Artificial Intelligence Double-Blind Method Antidepressive Agents Treatment Outcome Machine Learning Depression adolescents decision support tools Index. décimale : PER Périodiques Résumé : BACKGROUND: The treatment of depression in children and adolescents is a substantial public health challenge. This study examined artificial intelligence tools for the prediction of early outcomes in depressed children and adolescents treated with fluoxetine, duloxetine, or placebo. METHODS: The study samples included training datasets (N=271) from patients with major depressive disorder (MDD) treated with fluoxetine and testing datasets from patients with MDD treated with duloxetine (N=255) or placebo (N=265). Treatment trajectories were generated using probabilistic graphical models (PGMs). Unsupervised machine learning identified specific depressive symptom profiles and related thresholds of improvement during acute treatment. RESULTS: Variation in six depressive symptoms (difficulty having fun, social withdrawal, excessive fatigue, irritability, low self-esteem, and depressed feelings) assessed with the Children's Depression Rating Scale-Revised at 4-6 weeks predicted treatment outcomes with fluoxetine at 10-12 weeks with an average accuracy of 73% in the training dataset. The same six symptoms predicted 10-12 week outcomes at 4-6 weeks in (a) duloxetine testing datasets with an average accuracy of 76% and (b) placebo-treated patients with accuracies of 67%. In placebo-treated patients, the accuracies of predicting response and remission were similar to antidepressants. Accuracies for predicting nonresponse to placebo treatment were significantly lower than antidepressants. CONCLUSIONS: PGMs provided clinically meaningful predictions in samples of depressed children and adolescents treated with fluoxetine or duloxetine. Future work should augment PGMs with biological data for refined predictions to guide the selection of pharmacological and psychotherapeutic treatment in children and adolescents with depression. En ligne : http://dx.doi.org/10.1111/jcpp.13580 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=490 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 Deep Neural Network Reveals the World of Autism From a First-Person Perspective / Mindi RUAN in Autism Research, 14-2 (February 2021)
[article]
Titre : Deep Neural Network Reveals the World of Autism From a First-Person Perspective Type de document : Texte imprimé et/ou numérique Auteurs : Mindi RUAN, Auteur ; Paula J. WEBSTER, Auteur ; Xin LI, Auteur ; Shuo WANG, Auteur Article en page(s) : p.333-342 Langues : Anglais (eng) Mots-clés : artificial intelligence attention autism spectrum disorder deep neural network faces photos saliency Index. décimale : PER Périodiques Résumé : People with autism spectrum disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories, but was particularly successful at classifying photos of people with >80% accuracy. Importantly, visualization of our model revealed critical features that led to successful discrimination and showed that our model adopted a strategy similar to that of ASD experts. Furthermore, for the first time we showed that photos taken by individuals with ASD contained less salient objects, especially in the central visual field. Notably, our model outperformed classification of these photos by ASD experts. Together, we demonstrate an effective and novel method that is capable of discerning photos taken by individuals with ASD and revealing aberrant visual attention in ASD from a unique first-person perspective. Our method may in turn provide an objective measure for evaluations of individuals with ASD. LAY SUMMARY: People with autism spectrum disorder (ASD) demonstrate atypical visual attention to social stimuli. However, it remains largely unclear how they perceive the world from a first-person perspective. In this study, we employed a deep learning approach to analyze a unique dataset of photos taken by people with and without ASD. Our computer modeling was not only able to discern which photos were taken by individuals with ASD, outperforming ASD experts, but importantly, it revealed critical features that led to successful discrimination, revealing aspects of atypical visual attention in ASD from their first-person perspective. En ligne : http://dx.doi.org/10.1002/aur.2376 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=441
in Autism Research > 14-2 (February 2021) . - p.333-342[article] Deep Neural Network Reveals the World of Autism From a First-Person Perspective [Texte imprimé et/ou numérique] / Mindi RUAN, Auteur ; Paula J. WEBSTER, Auteur ; Xin LI, Auteur ; Shuo WANG, Auteur . - p.333-342.
Langues : Anglais (eng)
in Autism Research > 14-2 (February 2021) . - p.333-342
Mots-clés : artificial intelligence attention autism spectrum disorder deep neural network faces photos saliency Index. décimale : PER Périodiques Résumé : People with autism spectrum disorder (ASD) show atypical attention to social stimuli and aberrant gaze when viewing images of the physical world. However, it is unknown how they perceive the world from a first-person perspective. In this study, we used machine learning to classify photos taken in three different categories (people, indoors, and outdoors) as either having been taken by individuals with ASD or by peers without ASD. Our classifier effectively discriminated photos from all three categories, but was particularly successful at classifying photos of people with >80% accuracy. Importantly, visualization of our model revealed critical features that led to successful discrimination and showed that our model adopted a strategy similar to that of ASD experts. Furthermore, for the first time we showed that photos taken by individuals with ASD contained less salient objects, especially in the central visual field. Notably, our model outperformed classification of these photos by ASD experts. Together, we demonstrate an effective and novel method that is capable of discerning photos taken by individuals with ASD and revealing aberrant visual attention in ASD from a unique first-person perspective. Our method may in turn provide an objective measure for evaluations of individuals with ASD. LAY SUMMARY: People with autism spectrum disorder (ASD) demonstrate atypical visual attention to social stimuli. However, it remains largely unclear how they perceive the world from a first-person perspective. In this study, we employed a deep learning approach to analyze a unique dataset of photos taken by people with and without ASD. Our computer modeling was not only able to discern which photos were taken by individuals with ASD, outperforming ASD experts, but importantly, it revealed critical features that led to successful discrimination, revealing aspects of atypical visual attention in ASD from their first-person perspective. En ligne : http://dx.doi.org/10.1002/aur.2376 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=441 Sentiment Analysis in Children with Neurodevelopmental Disorders in an Ingroup/Outgroup Setting / E. VAUCHERET PAZ in Journal of Autism and Developmental Disorders, 50-1 (January 2020)
[article]
Titre : Sentiment Analysis in Children with Neurodevelopmental Disorders in an Ingroup/Outgroup Setting Type de document : Texte imprimé et/ou numérique Auteurs : E. VAUCHERET PAZ, Auteur ; M. MARTINO, Auteur ; M. HYLAND, Auteur ; M. CORLETTO, Auteur ; C. PUGA, Auteur ; M. PERALTA, Auteur ; N. DELTETTO, Auteur ; T. KUHLMANN, Auteur ; D. CAVALIE, Auteur ; M. LEIST, Auteur ; B. DUARTE, Auteur ; I. LASCOMBES, Auteur Article en page(s) : p.162-170 Langues : Anglais (eng) Mots-clés : Artificial intelligence Empathy Morality Neurodevelopmental disorders Social norms Third-party punishment Index. décimale : PER Périodiques Résumé : People punish transgressors with different intensity depending if they are members of their group or not. We explore this in a cross-sectional analytical study with paired samples in children with developmental disorders who watched two videos and expressed their opinion. In Video-1, a football-player from the participant's country scores a goal with his hand. In Video-2, a player from another country does the same against the country of the participant. Each subject watched the two videos and their answers were compared. The autism spectrum disorder (ASD) group showed negative feelings in Video 1 (M = - .1; CI 95% - .51 to .31); and in Video 2 (M = - .43; CI 95% .77 to - .09; t(8) = 1.64, p = .13), but the attention deficit hyperactivity disorder, learning disabilities, intellectual disability groups showed positive opinion in Video-1 and negative in Video-2. This suggests that children with ASD respect rules regardless of whether those who break them belong or not to their own group, possibly due to lower degrees of empathy. En ligne : http://dx.doi.org/10.1007/s10803-019-04242-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=414
in Journal of Autism and Developmental Disorders > 50-1 (January 2020) . - p.162-170[article] Sentiment Analysis in Children with Neurodevelopmental Disorders in an Ingroup/Outgroup Setting [Texte imprimé et/ou numérique] / E. VAUCHERET PAZ, Auteur ; M. MARTINO, Auteur ; M. HYLAND, Auteur ; M. CORLETTO, Auteur ; C. PUGA, Auteur ; M. PERALTA, Auteur ; N. DELTETTO, Auteur ; T. KUHLMANN, Auteur ; D. CAVALIE, Auteur ; M. LEIST, Auteur ; B. DUARTE, Auteur ; I. LASCOMBES, Auteur . - p.162-170.
Langues : Anglais (eng)
in Journal of Autism and Developmental Disorders > 50-1 (January 2020) . - p.162-170
Mots-clés : Artificial intelligence Empathy Morality Neurodevelopmental disorders Social norms Third-party punishment Index. décimale : PER Périodiques Résumé : People punish transgressors with different intensity depending if they are members of their group or not. We explore this in a cross-sectional analytical study with paired samples in children with developmental disorders who watched two videos and expressed their opinion. In Video-1, a football-player from the participant's country scores a goal with his hand. In Video-2, a player from another country does the same against the country of the participant. Each subject watched the two videos and their answers were compared. The autism spectrum disorder (ASD) group showed negative feelings in Video 1 (M = - .1; CI 95% - .51 to .31); and in Video 2 (M = - .43; CI 95% .77 to - .09; t(8) = 1.64, p = .13), but the attention deficit hyperactivity disorder, learning disabilities, intellectual disability groups showed positive opinion in Video-1 and negative in Video-2. This suggests that children with ASD respect rules regardless of whether those who break them belong or not to their own group, possibly due to lower degrees of empathy. En ligne : http://dx.doi.org/10.1007/s10803-019-04242-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=414