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

Public ISBD [article]  inJournal of Child Psychology and Psychiatry > 63-4 (April 2022) . - p.421-443 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 [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

Brief Report: Specificity of Interpersonal Synchrony Deficits to Autism Spectrum Disorder and Its Potential for Digitally Assisted Diagnostics / Jana Christina KOEHLER in Journal of Autism and Developmental Disorders, 52-8 (August 2022)  

Public ISBD [article]  inJournal of Autism and Developmental Disorders > 52-8 (August 2022) . - p.3718-3726 Titre : Brief Report: Specificity of Interpersonal Synchrony Deficits to Autism Spectrum Disorder and Its Potential for Digitally Assisted Diagnostics Type de document : Texte imprimé et/ou numérique Auteurs : Jana Christina KOEHLER, Auteur ; Alexandra Livia GEORGESCU, Auteur ; Johanna WEISKE, Auteur ; Moritz SPANGEMACHER, Auteur ; Lana BURGHOF, Auteur ; Peter FALKAI, Auteur ; Nikolaos KOUTSOULERIS, Auteur ; Wolfgang TSCHACHER, Auteur ; Kai VOGELEY, Auteur ; Christine M. FALTER-WAGNER, Auteur Article en page(s) : p.3718-3726 Langues : Anglais (eng) Mots-clés : Adult  Autism Spectrum Disorder/diagnosis  Humans  Mass Screening  Autism spectrum disorder  Diagnostics  Interpersonal synchrony  Motion energy analysis  Social interaction Index. décimale : PER Périodiques Résumé : Reliably diagnosing autism spectrum disorders (ASD) in adulthood poses a challenge to clinicians due to the absence of specific diagnostic markers. This study investigated the potential of interpersonal synchrony (IPS), which has been found to be reduced in ASD, to augment the diagnostic process. IPS was objectively assessed in videos of diagnostic interviews in a representative referral population from two specialized autism outpatient clinics. In contrast to the current screening tools that could not reliably differentiate, we found a significant reduction of IPS in interactions with individuals later diagnosed with ASD (n=16) as opposed to those not receiving a diagnosis (n=23). While these findings need to be validated in larger samples, they nevertheless underline the potential of digitally-enhanced diagnostic processes for ASD. En ligne : http://dx.doi.org/10.1007/s10803-021-05194-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=485 [article] Brief Report: Specificity of Interpersonal Synchrony Deficits to Autism Spectrum Disorder and Its Potential for Digitally Assisted Diagnostics [Texte imprimé et/ou numérique] / Jana Christina KOEHLER, Auteur ; Alexandra Livia GEORGESCU, Auteur ; Johanna WEISKE, Auteur ; Moritz SPANGEMACHER, Auteur ; Lana BURGHOF, Auteur ; Peter FALKAI, Auteur ; Nikolaos KOUTSOULERIS, Auteur ; Wolfgang TSCHACHER, Auteur ; Kai VOGELEY, Auteur ; Christine M. FALTER-WAGNER, Auteur . - p.3718-3726. Langues : Anglais (eng) in Journal of Autism and Developmental Disorders > 52-8 (August 2022) . - p.3718-3726 Mots-clés : Adult  Autism Spectrum Disorder/diagnosis  Humans  Mass Screening  Autism spectrum disorder  Diagnostics  Interpersonal synchrony  Motion energy analysis  Social interaction Index. décimale : PER Périodiques Résumé : Reliably diagnosing autism spectrum disorders (ASD) in adulthood poses a challenge to clinicians due to the absence of specific diagnostic markers. This study investigated the potential of interpersonal synchrony (IPS), which has been found to be reduced in ASD, to augment the diagnostic process. IPS was objectively assessed in videos of diagnostic interviews in a representative referral population from two specialized autism outpatient clinics. In contrast to the current screening tools that could not reliably differentiate, we found a significant reduction of IPS in interactions with individuals later diagnosed with ASD (n=16) as opposed to those not receiving a diagnosis (n=23). While these findings need to be validated in larger samples, they nevertheless underline the potential of digitally-enhanced diagnostic processes for ASD. En ligne : http://dx.doi.org/10.1007/s10803-021-05194-3 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=485

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