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Auteur Jianguo CAO |
Documents disponibles écrits par cet auteur (1)
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Commentary: Machine learning for autism spectrum disorder diagnosis - challenges and opportunities - a commentary on Schulte-Rüther et al. (2022) / Xu CAO in Journal of Child Psychology and Psychiatry, 64-6 (June 2023)
[article]
Titre : Commentary: Machine learning for autism spectrum disorder diagnosis - challenges and opportunities - a commentary on Schulte-Rüther et al. (2022) Type de document : Texte imprimé et/ou numérique Auteurs : Xu CAO, Auteur ; Jianguo CAO, Auteur Article en page(s) : p.966-967 Langues : Anglais (eng) Index. décimale : PER Périodiques Résumé : The commentary cites a study by Schulte-Rüther et al. (Journal of Child Psychology and Psychiatry, 2022) that proposed a machine learning model to predict a clinical best-estimate diagnosis of ASD when existing other co-occurring diagnoses. We discuss the valuable contribution of this work to developing a reliable computer-assisted diagnosis (CAD) system for ASD and point out that related research can be integrated with other multimodal machine learning methods. For future studies on developing the CAD system for ASD, we propose problems that need to be solved and potential research directions. En ligne : http://dx.doi.org/https://doi.org/10.1111/jcpp.13764 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=504
in Journal of Child Psychology and Psychiatry > 64-6 (June 2023) . - p.966-967[article] Commentary: Machine learning for autism spectrum disorder diagnosis - challenges and opportunities - a commentary on Schulte-Rüther et al. (2022) [Texte imprimé et/ou numérique] / Xu CAO, Auteur ; Jianguo CAO, Auteur . - p.966-967.
Langues : Anglais (eng)
in Journal of Child Psychology and Psychiatry > 64-6 (June 2023) . - p.966-967
Index. décimale : PER Périodiques Résumé : The commentary cites a study by Schulte-Rüther et al. (Journal of Child Psychology and Psychiatry, 2022) that proposed a machine learning model to predict a clinical best-estimate diagnosis of ASD when existing other co-occurring diagnoses. We discuss the valuable contribution of this work to developing a reliable computer-assisted diagnosis (CAD) system for ASD and point out that related research can be integrated with other multimodal machine learning methods. For future studies on developing the CAD system for ASD, we propose problems that need to be solved and potential research directions. En ligne : http://dx.doi.org/https://doi.org/10.1111/jcpp.13764 Permalink : https://www.cra-rhone-alpes.org/cid/opac_css/index.php?lvl=notice_display&id=504